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Wearable inertial sensors for human movement analysis: a five-year update
Abstract
Introduction
The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory-based ambulatory settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.
Areas covered
Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.
Expert Opinion
IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
... Here, we have discussed the role of MFA in providing an extra layer of security to every participating entity in the smart city ecosystem. Figure 10 shows the security areas with respect to the prime entities of a smart city [106][107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123][124] ...
... Here, we have discussed the role of MFA in providing an extra layer of security to every participating entity in the smart city ecosystem. Figure 10 shows the security areas with respect to the prime entities of a smart city [106][107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123][124]. ...
The overwhelming popularity of technology-based solutions and innovations to address day-to-day processes has significantly contributed to the emergence of smart cities. where millions of interconnected devices and sensors generate and share huge volumes of data. The easy and high availability of rich personal and public data generated in these digitalized and automated ecosystems renders smart cities vulnerable to intrinsic and extrinsic security breaches. Today, with fast-developing technologies, the classical username and password approaches are no longer adequate to secure valuable data and information from cyberattacks. Multi-factor authentication (MFA) can provide an effective solution to minimize the security challenges associated with legacy single-factor authentication systems (both online and offline). This paper identifies and discusses the role and need of MFA for securing the smart city ecosystem. The paper begins by describing the notion of smart cities and the associated security threats and privacy issues. The paper further provides a detailed description of how MFA can be used for securing various smart city entities and services. A new concept of blockchain-based multi-factor authentication named “BAuth-ZKP” for securing smart city transactions is presented in the paper. The concept focuses on developing smart contracts between the participating entities within the smart city and performing the transactions with zero knowledge proof (ZKP)-based authentication in a secure and privacy-preserved manner. Finally, the future prospects, developments, and scope of using MFA in smart city ecosystem are discussed.
... Wearable inertial sensors have evolved rapidly and are routinely used in different areas of clinical human movement analysis, e.g., gait analysis [9], stabilometry, instrumented clinical tests, upper-extremity mobility assessment, daily-life activity monitoring, and tremor assessment [10]. Such sensors have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings [11]. ...
... The best-fit FFNN models have shown a high correspondence with the OMC-driven MSK outputs in both SE (r avg = 0.90 ±0.19 and NRMSE avg = 0.33 ±0.18 ) and SN settings (r avg = 0.84 ±0.23 and NRMSE avg = 0.85 ±0.79 ) with comparable statistics for RNN models with r avg, SE = 0.89 ±0.17 , NRMSE avg, SE = 0.36 ±0.19 , r avg, SN = 0.78 ±0.23 and NRMSE avg, SN = 0.87 ±0.77 ). As can be expected, the SE models performed better than the SN models consistently across various ML model choices.To further aid in the interpretation of results, Figures 5 and 6 (along with SupplementaryFigures 10,11,12) show the correspondence between the NN-predicted outputs and the actual OMC-driven MSK outputs for the held-out test trial data. Most plots show excellent r (> 0.85) with notable exceptions, particularly for SN settings, in the case of Elbow Mediolateral, Elbow Anteroposterior, Wrist Anteroposterior, and Trunk Mediolateral (joint reaction forces) and Trunk Flexion/Extension and Trunk Internal/External rotation (joint moments). ...
Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into in vivo joint and muscle loading, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) systems are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one needs to use an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, we present an ML approach to map experimentally recorded IMC data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, we aim to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and searched for the best-fit model through an exhaustive search in the hyperparameters space in both subject-exposed (SE) & subject-naive (SN) settings. We observed a comparable performance for both FFNN & RNN models, which have a high degree of agreement (ravg, SE, FFNN = 0.90+/-0.19, ravg, SE, RNN = 0.89+/-0.17, ravg, SN, FFNN = 0.84+/-0.23, & ravg, SN, RNN = 0.78+/-0.23) with the desired OMC-driven MSK estimates for held-out test data. Mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.
... Smartwatches offer touchscreen access to apps (media, calculator, GPS, battery life), receiving and making calls, getting all types of phone's notifications, and recording heart rate and other vital signs. 35,[43][44][45][46][47] Companies like Apple offer smartwatches for daily use. Companies like Garmin Fenix offer smartwatches for specific/sensitive purposes, which are more optimised and rugged with trackers and sensors used to support back-country expeditions and Suunto smartwatches for scuba diving. ...
... Vendors or producers maintain this data and check for the ethical transfer while providing the authorized third party to perform analytics. 7,46,49,[77][78][79][80][81] Acts like Electronic Communications and Privacy Act (ECPA), Children's Online Privacy Act (COPA), and Federal Trade Commission (FTC) enforce laws for ethical data collection. ...
With the transforming world, awareness of lifestyle-based variation is necessary. The availability of the locally available network and smart devices like wearable health devices (WHDs) based on artificial intelligence (AI) technology prompted us to learn about the disease, its causes, spreads, and precautions. Socioeconomic, environmental and behavioural factors, international travel and migration foster and increase the spread of communicable diseases. Vaccine-preventable, foodborne, zoonotic, healthcare-related and communicable diseases pose significant threats to human health and may sometimes threaten international health security. On the other hand, non-communicable diseases, also known as chronic diseases, are more prolonged. It could be the cause of different factors like genetic, environmental, behavioural or physiological disturbances. Smart wearables help to keep these diseases in check through different sensors installed in them. They can check for the difference in body function, but they can also help the needy consult the physician or practitioner. The data collected from these devices can also check the current health status when compiled with data collected practically. Organizations viz., World Health Organization (WHO), Food and Drug Administration (FDA) work collaboratively, leading global efforts to expand health coverage. WHO keeps the nation safe through connecting its people on the health and awareness interactive platforms, and FDA promotes public health through supervision and control, defending its role in human health and services.
... Currently, movement analysis-particularly motion tracking-is usually performed using marker-based stereophotogrammetric optoelectronic systems integrated with force platforms, and used within controlled experimental environments [13,14]. However, over the past decade, the availability of wearable equipment has made it possible to conduct examinations in more ecological and daily-life conditions [15][16][17][18]. For instance, magnetoinertial measurement units (MIMUs) can provide a quantitative evaluation of movement by integrating information from triaxial accelerometers, gyroscopes, and magnetometers; these solutions are, in general, non-invasive and easy-to-use, allowing for continuous monitoring and self-assessment to evaluate and prevent risk, even at home. ...
Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques—such as Magnetic Resonance Imaging (MRI) and biplane radiography—should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches.
... In this context, particularly appealing is the possibility to employ wearable inertial sensors (i.e., inertial measurement units, IMUs) to obtain information about gait patterns based on trunk/limb acceleration data. Such devices gained popularity among the re searchers involved in the quantitative assessment of human movement [6,7] due to their miniaturized size, easiness of use, affordable cost and, above all, because they allow the overcoming of several limitations typical of motion capture systems, such as the need to have available a dedicated space, to perform specific preparations for the tested subjects (i.e., undressing, anthropometric collection data, marker placement, etc.) and the overall conditions in which the test takes place, which make this type of analysis uneconomical. In particular, the configuration, which consists of a unique sensor (usually located on the low back, close to the body's center of mass), is characterized by minimal encumbrance for the individual, fast setup and the possibility to perform tests in a variety of settings (including clinics, schools, gyms, outdoor settings), terrain conditions, overground or treadmill walking, straight or curved paths, etc. ...
Excessive body mass represents a serious threat to the optimal psychophysical develop ment of children, and it is known to be able to significantly affect their locomotor capabilities, mak ing them more prone to the development of musculoskeletal disorders. However, despite the rele vant number of existing studies, a clear gait pattern of overweight children has not been defined yet, particularly in the case of a mass excess that is relatively small (i.e., in those not obese). In the present study, we employed a wearable inertial measurement unit placed on the low back to derive spatio temporal parameters and quantify the smoothness of gait (by means of harmonic ratio) from trunk accelerations acquired during gait trials carried out by 108 children aged 6-10 (46% males), stratified into two groups according to their body mass index (normal weight, n = 69 and over weight, n = 39). The results show that while gait speed, stride length, cadence and double support duration were found to be almost identical in the two groups, significant differences were observed in terms of harmonic ratio. In particular, overweight children exhibited a reduced harmonic ratio in the antero posterior direction and higher harmonic ratio in the medio lateral direction. While the significantly lower harmonic ratio in the antero posterior direction is likely to be indicative of a loss of smoothness in the walking direction, probably due to a combination of factors associated with the altered movement biomechanics, the higher harmonic ratio in the medio lateral direction might be associated with specific strategies adopted to increase lateral stability. Although further studies are necessary to elucidate the specific mechanisms that influence the smoothness of gait, it is note worthy that harmonic ratios appear sensitive even to subtle change in locomotor control in over weight children characterized by apparently regular spatio temporal parameters of gait and might be employed to assess the effectiveness of interventions designed to improve mobility functions.
... Picerno et al. reviewed wearable inertial sensors for human movement analysis and concluded that, even though IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings, "the successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners. [13]" To better utilize the motion data obtained from IMU sensors for MSDs prevention, machine learning algorithms have been applied to automatically detect awkward postures [5,11,14,15]. In our previous research [5], we developed a wearable sensing system that integrates IMU sensors for motion sensing, a deep neural network (DNN) model for posture recognition, posture-based ergonomics assessment models for MSDs risk assessment, and user interface for risk assessment feedback. ...
Wearable technology has been playing an increasingly essential role in the construction domain, especially for safety and health related research. Musculoskeletal disorders (MSDs) are one of the most prevalent health problems among construction workers due to the physical demanding feature of the construction work. To solve this problem, wearable sensing technology has been applied for MSDs prevention. However, the large-scale adoption of wearables has encountered challenges and barriers. This study firstly reviewed recent literature on the factors influencing wearable technology adoption and designed a survey based on the review to further investigate adoption barriers and strategies using our proposed MSDs prevention system as a case study. The results demonstrate that the discomfort and fatigue caused by wearing devices for a long period of time is the main concerns hindering wearable adoption in our case. Construction managers expressed concerns on the indirect costs of implementation and workers expressed their concern on the invasion of privacy. To address these concerns, strategies to promote wearable adoption identified in literature such as worker training and education and providing personalized features were discussed. This study provides insight into the factors contributing to the large-scale adoption of wearable technology for MSDs prevention from the application perspective.
... In conclusion, the successful use of wearable devices for assessing mobility can be advantageous for both practitioners and scientists [62]. Gait characteristics obtained by wearables can be used to support tailored intervention rehabilitation and therapy plans [19,21,29,63]. ...
Background:
Fear of falling (FOF) is common in Parkinson's disease (PD) and associated with distinct gait changes. Here, we aimed to answer, how quantitative gait assessment can improve our understanding of FOF-related gait in hospitalized geriatric patients with PD.
Methods:
In this cross-sectional study of 79 patients with advanced PD, FOF was assessed with the Falls Efficacy Scale International (FES-I), and spatiotemporal gait parameters were recorded with a mobile gait analysis system with inertial measurement units at each foot while normal walking. In addition, demographic parameters, disease-specific motor (MDS-revised version of the Unified Parkinson's Disease Rating Scale, Hoehn & Yahr), and non-motor (Non-motor Symptoms Questionnaire, Montreal Cognitive Assessment) scores were assessed.
Results:
According to the FES-I, 22.5% reported low, 28.7% moderate, and 47.5% high concerns about falling. Most concerns were reported when walking on a slippery surface, on an uneven surface, or up or down a slope. In the final regression model, previous falls, more depressive symptoms, use of walking aids, presence of freezing of gait, and lower walking speed explained 42% of the FES-I variance.
Conclusion:
Our study suggests that FOF is closely related to gait changes in hospitalized PD patients. Therefore, FOF needs special attention in the rehabilitation of these patients, and targeting distinct gait parameters under varying walking conditions might be a promising part of a multimodal treatment program in PD patients with FOF. The effect of these targeted interventions should be investigated in future trials.
... During the last decades, wearable sensors have been developed for a wide range of medical applications [1][2][3]. The underlying technologies have reached a level of maturity allowing them to move from the laboratory and clinical research to a discussion of their application in clinical practice [4][5][6][7]. ...
... Beaucoup de méthodes sont décrites pour la détection d'évènements du cycle de marche, et ce domaine de recherche est très actif [129]. Cependant, aucun algorithme de détection des cycles de marche a partir de données représentant l'orientation en 3D de la hanche mesurée par un unique système de capteurs portatif n'a été identifié dans la littérature. ...
Cette thèse s’inscrit dans le contexte du projet e-Gait dont l’objectif est de développer un nouvel outil de mesure basé sur l’utilisation de systèmes numériques pour quantifier les troubles de la démarche de patients atteints de maladie neurodégénative, et plus particulièrement la Sclérose En Plaques (SEP). La solution adoptée consiste à mesurer les rotations en trois dimensions de la hanche au cours de la marche à l’aide d’un système de capteurs inertiels placé à la ceinture. Ces rotations sont représentées sous la forme d’une séquence de quaternions unitaires. Des méthodes adaptées à ce type de données sont présentées pour en extraire des informations relatives à la démarche de l’individu. Un algorithme est proposé pour segmenter le signal en cycles de marche. Dans une première approche, la démarche individuelle est représentée sous forme de paramètres spatio- temporels. Dans une seconde, elle est représentée sous la forme d’une unique séquence de quaternions unitaire appelée "Signature de Marche" (SdM). Des méthodes de classification non supervisée et semi-supervisée sont adaptées pour permettre d’identifier des groupes de patients présentant des déficits de la marche similaires à partir de leur SdM
... During the last decades, wearable sensors have been developed for a wide range of medical applications [1][2][3]. The underlying technologies have reached a level of maturity allowing them to move from the laboratory and clinical research to a discussion of their application in clinical practice [4][5][6][7]. ...
Wearable sensors offer the opportunity for patients to perform a self-assessment of their function with respect to a variety of movement exercises. Corresponding commercial products have the potential to change the communication between patients and physiotherapists during the recovery process. Even if they turn out to be user-friendly, there remains the question to what degree the numerical results are reliable and comparable with those obtained by assessment methods traditionally used. To address this question for one specific recently developed and commercially available sensor, a method comparison study was performed. The sensor-based assessment of eight movement parameters was compared with an assessment of the same parameters based on test procedures traditionally used. Thirty-three patients recovering after arthroscopic knee surgery participated in the study. The whole assessment procedure was repeated. Reproducibility and agreement were quantified by the intra class correlation coefficient. The height of a one-leg vertical jump and the number of side hops showed high agreement between the two modalities and high reproducibility (ICC > 0.85). Due to differences in the set-up of the assessment, agreement could not be achieved for three mobility parameters, but even the correlation was only fair (r < 0.5). Knee stability showed poor agreement. Consequently, the use of the sensor can currently only be recommended for selected parameters. The variation in degree of agreement and reproducibility across different parameters clearly indicate the need for developing corresponding guidance for each new sensor put onto the market.
... Tele-rehabilitation via online video communication is an emerging area that is attracting increasing attention as a potential alternative to conventional, face-to-face rehabilitation; it is suggested to be an option for people located remotely to reduce the need for frequent travel [2]. A critical missing element in current tele-rehabilitation solutions is the tele-assessment component which supports an objective remote assessment of functional performance-more specifically, the range of motion (ROM) of joints-integrated with web-based management and planning capabilities [3]. The quantification of hip ROM is a key clinical measurement [4,5] that is performed before and after surgical intervention to the hip [6]. ...
Background:
Tele-health has become a major mode of delivery in patient care, with increasing interest in the use of tele-platforms for remote patient assessment. The use of smartphone technology to measure hip range of motion has been reported previously, with good to excellent validity and reliability. However, these smartphone applications did not provide real-time tele-assessment functionality. We developed a novel smartphone application, the TelePhysio app, which allows the clinician to remotely connect to the patient's device and measure their hip range of motion in real time. The aim of this study was to investigate the concurrent validity and between-sessions reliability of the TelePhysio app. In addition, the study investigated the concurrent validity, between-sessions, and inter-rater reliability of a second tele-assessment approach using video analysis.
Methods:
Fifteen participants (nfemales = 6) were assessed in our laboratory (session 1) and at their home (session 2). We assessed maximum voluntary active hip flexion in supine and hip internal and external rotation, in both prone and sitting positions. TelePhysio and video analysis were validated against the laboratory's 3-dimensional motion capture system in session 1, and evaluated for between-sessions reliability in session 2. Video analysis inter-rater reliability was assessed by comparing the analysis of two raters in session 2.
Results:
The TelePhysio app demonstrated high concurrent validity against the 3D motion capture system (ICCs 0.63-0.83) for all hip movements in all positions, with the exception of hip internal rotation in prone (ICC = 0.48, p = 0.99). The video analysis demonstrated almost perfect concurrent validity against the 3D motion capture system (ICCs 0.85-0.94) for all hip movements in all positions, with the exception of hip internal rotation in prone (ICC = 0.44, p = 0.01). The TelePhysio and video analysis demonstrated good between-sessions reliability for hip external rotation and hip flexion, ICC 0.64 and 0.62, respectively. The between-sessions reliability of hip internal and external rotation for both TelePhysio and video analysis was fair (ICCs 0.36-0.63). Inter-rater reliability ICCs for the video analysis were 0.59 for hip flexion and 0.87-0.95 for the hip rotation range.
Conclusions:
Both tele-assessment approaches, using either a smartphone application or video analysis, demonstrate good to excellent concurrent validity, and moderate to substantial between-sessions reliability in measuring hip rotation and flexion range of motion, but less in internal hip rotation in the prone position. Thus, it is recommended that the seated position be used when assessing hip internal rotation. The use of a smartphone to remotely assess hip range of motion is an appropriate, effective, and low-cost alternative to the face-to-face assessments. This method provides a simple, cost effective, and accessible patient assessment tool with no additional cost. This study validates the use of smartphone technology as a tele-assessment tool for remote hip range of motion assessment.
... Motion sensors are usually used to monitor human behavior [10][11][12]. In particular, the monitoring of body posture can effectively reduce the risk of falls and assess the behavioral disorders of the elderly [13,14]. Skin-attachable heart rate, respiration, and oxygen saturation sensors provide real-time monitoring of cardiovascular and respiratory disease conditions [15]. ...
With the growing concern about human health issues, especially during the outbreak of the COVID-19 pandemic, the demand for personalized healthcare regarding disease prevention and recovery is increasing. However, tremendous challenges lie in both limited public medical resources and costly medical diagnosis approaches. Recently, skin-attachable sensors have emerged as promising health monitoring platforms to overcome such difficulties. Owing to the advantages of good comfort and high signal-to-noise ratio, skin-attachable sensors enable household, real-time, and long-term detection of weak physiological signals to efficiently and accurately monitor human motion, heart rate, blood oxygen saturation, respiratory rate, lung and heart sound, glucose, and biomarkers in biomedical applications. To further improve the integration level of biomedical skin-attachable sensors, efforts have been made in combining multiple sensing techniques with elaborate structural designs. This review summarizes the recent advances in different functional skin-attachable sensors, which monitor physical and chemical indicators of the human body. The advantages, shortcomings, and integration strategies of different mechanisms are presented. Specially, we highlight sensors monitoring pulmonary function such as respiratory rate and blood oxygen saturation for their potential usage in the COVID-19 pandemic. Finally, the future development of skin-attachable sensors is envisioned.
... At present, researchers have designed a variety of human motion intention recognition technologies to improve the rehabilitation effect and efficiency of patients [9]. In the next five years, wearable inertial sensors will be widely accepted and used in clinical environment, not only to evaluate human mobility, motor performance and motor disorders, but also to extend to cognitive training based on home mobile health [10]. In the recognition of human motion, it is very important to design an intelligent wearable device that can provide recognition help [11,12]. ...
In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids. This paper proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient’s movement. Firstly, a multi-sensor data acquisition system is designed according to the rehabilitation needs of the patient and the movement characteristics of the human body. A mathematical model of movement behavior is then established. By analyzing and processing motion data, the change in the center of gravity of the human body and the behavior intention signal are derived and used as a control command for the robot to follow the human body’s movement. Secondly, in order to improve the control effect of rehabilitation robot following human motion, an adaptive radial basis function neural network sliding mode controller (ARBFNNSMC) is designed based on the robot dynamic model. The adaptive adjustment of switching gain coefficient is performed by radial basis function neural network. The controller can overcome the influence caused by the change of robot control system parameters due to the fluctuation of the center of gravity of human body, enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human body motion. Finally, the motion following experiment of the rehabilitation robot is performed. The experimental results show that the robot can recognize the motion intention of human body and perform the training goal of following different subjects to complete straight lines and curves. The correctness of human motion behavior model and robot control algorithm is verified, which shows the feasibility of the intelligent control method proposed in this paper.
... Likewise, the number of literature reports on these body-worn devices has surged and these devices collect a wide variety of motion-related data to provide wearers with automated evaluated biofeedback (Peake, Kerr, & Sullivan, 2018). Notably, the use of wearables is also changing rapidly in the clinical area where it has rapidly evolved from safe settings in laboratories to applied use in unsupervised practice (Picerno et al., 2021). Despite this rapid progress (including in sports), it is often unclear whether these devices meet the criteria of test validity, reliability, and objectivity. ...
Wearables are commonly used in practice for measuring and monitoring performance in high-level sports. That being said, they are often designed and intended for use during sports conducted on rigid surfaces. As such, sports that are conducted on sand, e.g. beach volleyball, lack equipment that can be specifically applied in the field. Therefore, the aim of this study was to develop and validate an inertial measurement unit (IMU)-based system for automatic jump detection and jump height measurement in sand. The system consists of two IMUs, which were attached to different parts of the athletes’ bodies. For validation under laboratory conditions, 20 subjects each performed five jumps on two consecutive days in a sandbox placed on force plates. Afterwards, five beach volleyball athletes performed complex combinations of beach volleyball-specific movements and jumps wearing the IMUs whilst being video recorded simultaneously. This was conducted in an ecologically valid setting to determine the validity of the IMU to correctly detect jumping actions. The results of the laboratory tests show excellent day-to-day reliability (intraclass correlation coefficient [ICC] = 0.937, two-way mixed effects, single measurement, consistency) and excellent concurrent validity (ICC = 0.946, two-way mixed effects, single rater, absolute agreement) compared to the gold standard (force plates). The accuracy in jump detection of the IMU was 100 and 97.5% in the laboratory and ecologically valid settings, respectively. Although there are still some aspects to consider when using such devices, the current findings provide recommendations regarding best practice when using such a device on a variable and unstable surface. Collectively, such a device could be applied in the field to provide coaches and practitioners with direct feedback to monitor training or match play.
... It is possible to collect data without hindering the natural flow of the outdoor activities. Their calibration process is simple when compared to opto-electronic marker-based systems [7][8][9]. IMU-based systems consist of accelerometers, gyroscopes, and magnetometers [10]. The accelerometer measures the linear acceleration, integrating to calculate the position. ...
Extracting data from {Zhu, 2019 #5} daily life activities is important in biomechanical applications to define exact boundary conditions for the intended use-based applications. Although optoelectronic camera-marker based systems are used as gold standard tools for medical applications, due to line-of-sight problem, there is a need for wearable, affordable motion capture (MOCAP) systems. We investigate the potential use of a wearable inertial measurement unit (IMU) based-wearable MOCAP system for biomechanical applications. The in vitro proof of concept is provided for the full lower body consisting of hip, knee, and ankle joints via controlled single-plane anatomical range of motion (ROM) simulations using an electrical motor, while collecting data simultaneously via opto-electronic markers and IMU sensors. On 15 healthy volunteers the flexion-extension, abduction-adduction, internal-external rotation (ROM) values of hip and, the flexion – extension ROM values of the knee and ankle joints are calculated for both systems. The Bland-Altman graphs showed promising agreement both for in vitro and in vivo experiments. The maximum Root Mean Square Errors (RMSE) between the systems in vitro was 3.4° for hip and 5.9° for knee flexion motion in vivo , respectively. The gait data of the volunteers were assessed between the heel strike and toe off events to investigate the limits of agreement, calculating the population averages and standard deviation for both systems over the gait cycle. The maximum difference was for the ankle joint <6°. The results show that proposed system could be an option as an affordable-democratic solution.
... Accordingly, severe hypoglycemia can cause death among young people with type one diabetes. Tremor is one of the widespread symptoms that typically begin to occur when the blood sugar level goes below the normal level of 70 mg/dL [7,8]. According to [9], the hand tremor frequency is estimated between the value of 4 to 9 Hz, while [10] shows that the actual tremor can be detected between 4 to 12 Hz. ...
Diabetes is one of the lifelong diseases that require systematic medical care to avoid life-menacing ramifications. Uncontrolled diabetes can cause severe damage to most internal body organs, probably leading to death. Particularly, nocturnal hypoglycemic that occur usually at night during sleep. Severe cases of these events can lead to seizures, fainting, loss of consciousness, and death. The current medical devices lack to give the warning to reduce the risk of acquiring nocturnal hypoglycemic events because they use only for glucose monitoring during waking times. Consequently, the main goal of this work is to design and implement a new wearable device to detect and monitor tremors, which occur when a user has hypoglycemia (low blood sugar). The device can detect a frequency range of 4–12 Hz by using the accelerometer of Arduino Nano 33 BLE. It can send a signal to the phone application (app) via Bluetooth Low Energy (BLE). Once the phone receives a signal, the phone application can activate an alarm system to wake up the patient, call three selected contacts number, and universal emergency number. In case of the user is unresponsive, the app can provide the patient’s location, name, and date of birth to the emergency contacts numbers and universal emergency number. Additionally, the device cost is economically feasible and competitive compared to other medical devices.
... Future robots should also provide kinematic and kinetic information extracted by arm and hand trajectories and torques, respectively. The technology for embedding sensors with this scope in the robots already exist, such as wearable devices for motion capture [24] and for interacting with virtual reality [25]. ...
In the past two decades, many studies reported the efficacy of upper limb robotic rehabilitation in patients after stroke, also in its chronic phase. Among the possible advantages of robotic therapy over conventional therapy are the objective measurements of kinematic and kinetic parameters during therapy, such as the spatial volume covered by the patient’s upper limb and the weight support provided by the robot. However, the clinical meaning and the usability of this information is still questioned. Forty patients with chronic stroke were enrolled in this study and assessed at the beginning of upper limb robotic therapy (Armeo® Power) and after two weeks (ten sessions) of therapy by recording the working volume and weight support provided by the robot and by administering six clinical scales to assess upper limb mobility, strength, spasticity, pain, neurological deficits, and independency. At baseline, the working volume significantly correlated with spasticity, whereas weight support significantly correlated with upper limb strength, pain, spasticity, and neurological deficits. After two weeks of robotic rehabilitation, all the clinical scores as well as the two parameters improved. However, the percentage changes in the working volume and weight support did not significantly correlate with any of the changes in clinical scores. These results suggest caution in using the robotic parameters as outcome measures because they could follow the general improvement of the patient, but complex relationships with clinical features are possible. Robotic parameters should be analyzed in combination with the clinical scores or other objective measures because they may be informative about therapy progression, and there is a need to combine their clinical, neuroscientific, and biomechanical results to avoid misleading interpretations.
Objective:
Poor ergonomic posture during interventional procedures might lead to increased physical discomfort and work-related musculoskeletal disorders. Adjunctive equipment such as lead aprons (LAs) has been shown to increase ergonomic posture risk (EPR). The objective of this study was to evaluate the effectiveness of StemRad MD (StemRad Ltd., Tel Aviv, Israel), a weightless exoskeleton-based radiation protective ensemble, in reducing EPR on the operator using wearable inertial measurement unit (IMU) sensors.
Methods:
A prospective, observational study was conducted at an academic hospital. Inertial measurement unit sensors were affixed to the upper back of 9 interventionalists to assess ergonomic risk posture during endovascular procedures while wearing a traditional LA or the StemRad MD radiation protection system. Total fluoroscopy time, procedure type, and ergonomic risk postures were recorded and analyzed.
Results:
Twenty-one cases were performed with StemRad MD and 30 with LAs. Mean procedure time for the StemRad MD procedures was 48.4±23.3 minutes (range: 24-106 min), and for LA procedures, it was 34.66±25.83 minutes (range: 6-100 min) (p=.060). The operators assumed low-risk ergonomic positions in 96.1% of StemRad MD cases and in 62.9% of LA cases (p=.001), and high-risk ergonomic positions in 0% and 6.2%, respectively (p=.80). Mean EPR score for StemRad MD was 1.16, and for the LA, it was 1.49 (p=.001).
Conclusions:
StemRad MD significantly reduces the EPR to the torso compared with a LA-based radiation protection system.
Clinical impact:
Poor ergonomic posture during interventional procedures might leas to work-related musculoskeletal disorders for healthcare workers. StemRad MD, a weightless, exoskeleton-based radiation protection system was shown to significantly reduce ergonomic posture risk to the torso compared to conventional lead aprons. This might lead to reduced physical discomfort for procedure-based specialists.
Kinematics obtained using Inertial Measurement Units (IMUs) still present significant differences when compared to those obtained using optoelectronic systems. Multibody Optimization (MBO) might diminish these differences by reducing soft-tissue artefacts - probably emphasized when using IMUs - as established for optoelectronic-based kinematics. To test this hypothesis, 15 subjects were equipped with 7 IMUs and 38 reflective markers tracked by 18 optoelectronic cameras. The subjects walked, ran, cycled on an ergocycle, and performed a task which induced joint movements in the transverse and frontal planes. In addition to lower-body kinematics computed using the optoelectronical system data, three IMU-based kinematics were computed: from IMU orientations without MBO; from MBO performed using the OpenSense add-on of the OpenSim software (OpenSim 4.2, Stanford, USA); as outputs from the commercialised MVN MBO (Xsens, Netherlands). Root Mean Square Errors (RMSE), coefficients of correlations, and differences in range of motion were calculated between the three IMU-based methods and the reference kinematics. MVN MBO seems to present a slight advantage over Direct kinematics or OpenSense MBO, since it presents 34 times out of 48 (12 degrees of freedom * 4 sports activities) a mean RMSE inferior to the Direct and OpenSense kinematics. However, it was not always significant and the differences rarely exceeded 2°. This study does not therefore conclude on a significant contribution of MBO in improving lower-body kinematics obtained using IMUs. This lack of results can partly be explained by the weakness of both the kinematic constraints applied to the kinematic chain and segment stiffening. Personalization of the kinematic chain, the use of more than one IMU by segment in order to provide information redundancy, or the use of other approaches based on the Kalman Filter might increase this MBO impact.
O objetivo do presente estudo foi desenvolver uma revisão bibliográfica, envolvendo os temas IA para Análise do Movimento Humano, Prototipagem para Análise do Movimento Humano e Acelerômetro controlado por K-NN para análise do Movimento Humano, de forma crítica para expandir a discussão sobre o tema. Este artigo apresenta uma revisão bibliográfica de cunho qualitativo, onde buscou-se dados científicos que pudessem embasar uma maior discussão crítica sobre os itens propostos. A IA é uma ferramenta tecnológica que está em um momento de pleno desenvolvimento, estando distante de ter uma desaceleração na descoberta de novas aplicações; a prototipagem é uma possibilidade para pesquisadores desenvolverem estudos e aumentarem as possibilidades de aplicação da IA; o acelerômetro controlado por K-NN está demonstrando ser uma ferramenta na busca por estudos que possam avaliar atividade física em vida livre, porém ainda não existe clareza sobre o tema, ainda mais ao tratar sobre exercício físico.
Human activity monitoring based on wearable sensors is important in a wide range of biomedical and healthcare applications. Existing wearable sensors using a single unit cannot capture the movements of all body segments. This article presents a novel wearable electrostatic sensor that can detect limb and torso movements during routine daily activities from any location on the body. Because the electric potential of the human body varies during movements, the sensor measures the potential difference between the body and the electrode for motion sensing. A charge amplifier converts the induced charge on the electrode into a voltage signal, which is further amplified, filtered, digitalized, and transmitted via ZigBee. The experimental assessment was carried out by collecting sensor signals from three locations simultaneously while the subject performed different movements. The capability of the sensor to capture limb and torso movements from any location is validated. The characteristics of the sensor are quantified by correlating the sensor signal to simple and cyclic movements. It is found that the sensor signal depends on the sensor’s mounting location on the body, the type of activity, and various factors.
Background
Detailed understanding of impairments that underlie walking dysfunction through objective measures is essential to diagnosis, evaluation and care planning. Despite significant developments in motion tracking technologies, there is a dearth of research about the influence of remote monitoring context on performance. The objective of this study was to determine whether gait parameters collected by the OneStep smartphone application differ based on the recording condition.
Methods
Retrospective repeated measures univariate analysis was performed on data extracted based on detected activity, either spontaneous (background recording) or consciously initiated (in app) walks, of 25 patients enrolled in a physical therapy program.
Findings
Across 7227 walking bouts, significant differences between the two paradigms in velocity (g = 0.48), double support (g = 0.37), stride length (g = 0.37) and step length of the affected side (g = 0.32) were revealed. Overall, the passively recorded walks presented a less clinically favorable spatiotemporal pattern for each of these variables.
Interpretation
The recording context of walks that were used for analysis appears to significantly affect the biomechanical output of the OneStep application. It is unclear whether the disparity found would impact functional recovery of individuals undergoing rehabilitation due to neurological or musculoskeletal disorder. Clinicians may consider this information when incorporating remotely-acquired quantitative gait analysis and interpreting care outcomes as part of therapeutic practice. Future work can further investigate the behavioral and environmental factors contributing to how movement occurs in specific clinical populations when monitored via mobile health systems.
Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of the durations of specific walking gait subphases. Such harmonic proportions may be affected—partially or even totally destroyed—by several neurological and/or systemic disorders, thus drastically reducing the smooth, graceful, and melodic flow of movements and altering gait self-similarities. Aim. In this paper we aim at, preliminarily, showing the reliability of a technologically assisted methodology—performed with an easy to use wearable motion capture system—for the evaluation of motion abilities in Ataxia-Telangiectasia (AT), a rare infantile onset neurodegenerative disorder, whose typical neurological manifestations include progressive gait unbalance and the disturbance of motor coordination. Methods. Such an experimental methodology relies, for the first time, on the most recent accurate and objective outcome measures of gait recursivity and harmonicity and symmetry and double support subphase consistency, applied to three AT patients with different ranges of AT severity. Results. The quantification of the level of the distortions of harmonic temporal proportions is shown to include the qualitative evaluations of the three AT patients provided by clinicians. Conclusions. Easy to use wearable motion capture systems might be used to evaluate AT motion abilities through recursivity and harmonicity and symmetry (quantitative) outcome measures.
A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke.
Taiwan has been successful in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic; however, without a vaccine the threat of a second outbreak remains. Young adults who show few to no symptoms when infected have been identified in many countries as driving the virus’ spread through unidentifiable community transmission. Mobile tracking technologies register nearby contacts of a user and notifies them if one later tests positive to the virus, potentially solving this issue; however, the effectiveness of these technologies depends on their acceptance by the public. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google’s Bluetooth exposure notification system) among four samples of young Taiwanese adults (aged 25 years or younger). Using Bayesian methods, we find high acceptance for all three tracking technologies (>75%), with acceptance for each technology surpassing 90% if additional privacy measures were included. We consider the policy implications of these results for Taiwan and similar cultures.
In response to the COVID-19 pandemic, many Governments are instituting mobile tracking technologies to perform rapid contact tracing. However, these technologies are only effective if the public is willing to use them, implying that their perceived public health benefits must outweigh personal concerns over privacy and security. The Australian federal government recently launched the ‘COVIDSafe’ app, designed to anonymously register nearby contacts. If a contact later identifies as infected with COVID-19, health department officials can rapidly followup with their registered contacts to stop the virus’ spread. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google’s Bluetooth exposure notification system) in two representative samples of the Australian public prior to the launch of COVIDSafe. We compared these attitudes to usage of the COVIDSafe app after its launch in a further two representative samples of the Australian public. Using Bayesian methods, we find widespread acceptance for all tracking technologies, however, observe a large intention-behaviour gap between people’s stated attitudes and actual uptake of the COVIDSafe app. We consider the policy implications of these results for Australia and the world at large.
Ensuring proper dosage of treatment and repetition over time is a major challenge in neurorehabilitation. However, a requirement of physical distancing to date compromises their achievement. While mostly associated to COVID-19, physical distancing is not only required in a pandemic scenario, but also advised for several clinical conditions (e.g. immunocompromised individuals) or forced for specific social contexts (e.g. people living in remote areas worldwide). All these contexts advocate for the implementation of alternative healthcare models. The objective of this perspective is to highlight the benefits of remote administration of rehabilitative treatment, namely telerehabilitation, in counteracting physical distancing barriers in neurorehabilitation. Sustaining boosters of treatment outcome, such as compliance, sustainability, as well as motivation, telerehabilitation may adapt to multiple neurological conditions, with the further advantage of a high potential for individualization to patient’s or pathology’s specificities. The effectiveness of telerehabilitation can be potentiated by several technologies available to date: virtual reality can recreate realistic environments in which patients may bodily operate, wearable sensors allow to quantitatively monitor the patient’s performance, and signal processing may contribute to the prediction of long-term dynamics of patient recovery. Telerehabilitation might spark its advantages far beyond the mere limitation of physical distancing effects, mitigating criticalities of daily neurorehabilitative practice, and thus paving the way to the envision of mixed models of care, where hospital-based procedures are complementarily integrated with telerehabilitative ones.
Sensor-based technological therapy devices could be a possible neurorehabilitation strategy for motor rehabilitation in patients with stroke during the post-acute hospitalization, especially for treating upper extremities function limitations. The audio-visual feedback devices are characterized by interactive therapy games that allow training the movement of shoulders, elbows, and wrist, measuring the strength and the active range of motion of upper limb, registering data in an electronic database to quantitatively monitoring measures and therapy progress. This study aimed to investigate the effects of sensor-based motor rehabilitation in add-on to the conventional neurorehabilitation for improving the upper limb functions in patients with subacute stroke. Thirty-seven patients were enrolled in the study and randomly assigned to the experimental group and the control group. The training consisting of twelve sessions of upper limb training compared with twelve sessions of upper limb sensory-motor training, without robotic support. Both rehabilitation programs were performed for 40 minutes three times a week, for 4 weeks, in addition to conventional therapy. All patients were evaluated at the baseline (T0) and after 4 weeks of training (T1). The within-subject analysis showed a statistically significant improvement in both groups in all clinical scales. The analysis of effectiveness revealed that, compared with baseline (T0), the improvement percentage in the Modified Barthel Index was greater in the experimental group than the control group. The use of a sensor-based training with audio-video-feedback could be a useful complementary strategy for improving upper limb motor functions in patients with stroke during post-acute neurorehabilitation.
Introduction
The Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting.
Methods
(1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6).
Results
(1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than ‘physical activity’ for the data we are collecting (Q1); make only minor changes to ProPASS’s accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6).
Conclusions
Thigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS’s strategic directions—indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing.
Objective
To study the U.S. public’s attitudes toward surveillance measures aimed at curbing the spread of COVID-19, particularly smartphone applications (apps) that supplement traditional contact tracing.
Method
We deployed a survey of approximately 2,000 American adults to measure support for nine COVID-19 surveillance measures. We assessed attitudes toward contact tracing apps by manipulating six different attributes of a hypothetical app through a conjoint analysis experiment.
Results
A smaller percentage of respondents support the government encouraging everyone to download and use contact tracing apps (42%) compared with other surveillance measures such as enforcing temperature checks (62%), expanding traditional contact tracing (57%), carrying out centralized quarantine (49%), deploying electronic device monitoring (44%), or implementing immunity passes (44%). Despite partisan differences on a range of surveillance measures, support for the government encouraging digital contact tracing is indistinguishable between Democrats (47%) and Republicans (46%), although more Republicans oppose the policy (39%) compared to Democrats (27%). Of the app features we tested in our conjoint analysis experiment, only one had statistically significant effects on the self-reported likelihood of downloading the app: decentralized data architecture increased the likelihood by 5.4 percentage points.
Conclusion
Support for public health surveillance policies to curb the spread of COVID-19 is relatively low in the U.S. Contact tracing apps that use decentralized data storage, compared with those that use centralized data storage, are more accepted by the public. While respondents’ support for expanding traditional contact tracing is greater than their support for the government encouraging the public to download and use contact tracing apps, there are smaller partisan differences in support for the latter policy.
Introduction
Cardiac rehabilitation (CR) is recommended for secondary prevention of cardiovascular disease and reducing the risk of repeat cardiac events. Physical activity is a core component of CR; however, studies show that participants remain largely sedentary. Sedentary behaviour is an independent risk factor for all-cause mortality. Strategies to encourage sedentary behaviour change are needed. This study will explore the effectiveness and costs of a smartphone application (Vire) and an individualised online behaviour change program (ToDo-CR) in reducing sedentary behaviour, all-cause hospital admissions and emergency department visits over 12 months after commencing CR.
Methods and analysis
A multicentre, assessor-blind parallel randomised controlled trial will be conducted with 144 participants (18+ years). Participants will be recruited from three phase-II CR centres. They will be assessed on admission to CR and randomly assigned (1:1) to one of two groups: CR plus the ToDo-CR 6-month programme or usual care CR. Both groups will be re-assessed at 6 months and 12 months for the primary outcome of all-cause hospital admissions and presentations to the emergency department. Accelerometer-measured changes in sedentary behaviour and physical activity will also be assessed. Logistic regression models will be used for the primary outcome of hospital admissions and emergency department visits. Methods for repeated measures analysis will be used for all other outcomes. A cost-effectiveness analysis will be conducted to evaluate the effects of the intervention on the rates of hospital admissions and emergency department visits within the 12 months post commencing CR.
Ethics and dissemination
This study received ethical approval from the Australian Capital Territory Health (2019.ETH.00162), Calvary Public Hospital Bruce (20–2019) and the University of Canberra (HREC-2325) Human Research Ethics Committees (HREC). Results will be disseminated through peer-reviewed academic journals. Results will be made available to participants on request.
Trial registration number
ACTRN12619001223123.
Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.
Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.
Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.
ABSTRACT
Background: Telerehabilitation (TR) in chronic stroke patients has emerged as a promising modality to deliver rehabilitative treatmentat-
home. The primary objective of our methodical clinical study was to determine the efficacy of a novel rehabilitative device in terms of
recovery of function in daily activities and patient satisfaction and acceptance of the medical device provided.
Methods: A 12-week physiotherapy program (balance exercises, upper and lower limb exercises with specific motor tasks using a biofeedback
system and exergaming) was administered using the WeReha device. Twenty-five (N = 25) chronic stroke outpatients were enrolled,
and the data of 22 patients was analyzed. Clinical data and functional parameters were collected by Berg Balance scale (BBS), Barthel
Index (BI), Fugl-Meyer scale (FM) , Modified Rankin scale (mRS), and Technology Acceptance Model (TAM) questionnaire at baseline (T0),
after treatment (T1), and at the 12-week follow-up (T2). Statistical tests were used to detect significant differences (P < .05), and Cohen’s (Co)
value was calculated.
Results : BI scores improved significantly after treatment (P = .036; Co 0.776, medium), as well as BBS scores (P = .008; Co 1.260, high).
The results in FM scale (P = .003) and mRS scores (P = .047) were significant post treatment. Follow-up scores remained stable across all
scales, except the BI. The A and C sub-scales of the TAM correlated significantly to only a T2 to T1 difference for BI scores with P = .021 and
P = .042.
Conclusion: Currently, the WeReha program is not the conventional therapy for stroke patients, but it could be an integrative telerehabilitative
resource for such patients as a conventional exercise program-at-home.
Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.
Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.
Inertial measurement units (IMUs) have been increasingly popular in rehabilitation research. However, despite their accessibility and potential advantages, their uptake and acceptance by health professionals remain a big challenge. The development of an IMU-based clinical tool must bring together engineers, researchers and clinicians. This study is part of a developmental process with the investigation of clinicians’ perspectives about IMUs. Clinicians from four rehabilitation centers were invited to a 30-minute presentation on IMUs. Then, two one-hour focus groups were conducted with volunteer clinicians in each rehabilitation center on: 1) IMUs and their clinical usefulness, and 2) IMUs data analysis and visualization interface. Fifteen clinicians took part in the first focus groups. They expressed their thoughts on: 1) categories of variables that would be useful to measure with IMUs in clinical practice, and 2) desired characteristics of the IMUs. Twenty-three clinicians participated to the second focus groups, discussing: 1) functionalities, 2) display options, 3) clinical data reported and associated information, and 4) data collection duration. Potential influence of IMUs on clinical practice and added value were discussed in both focus groups. Clinicians expressed positive opinions about the use of IMUs, but their expectations were high before considering using IMUs in their practice.
Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (≥60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cutoff values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.
Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in "real-world" conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents' background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.
This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.
Parkinson's disease (PD) management requires the involvement of movement disorders experts, other medical specialists, and allied health professionals. Traditionally, multispecialty care has been implemented in the form of a multidisciplinary center, with an inconsistent clinical benefit and health economic impact. With the current capabilities of digital technologies, multispecialty care can be reshaped to reach a broader community of people with PD in their home and community. Digital technologies have the potential to connect patients with the care team beyond the traditional sparse clinical visit, fostering care continuity and accessibility. For example, video conferencing systems can enable the remote delivery of multispecialty care. With big data analyses, wearable and non-wearable technologies using artificial intelligence can enable the remote assessment of patients' conditions in their natural home environment, promoting a more comprehensive clinical evaluation and empowering patients to monitor their disease. These advances have been defined as technology-enabled care (TEC). We present examples of TEC under development and describe the potential challenges to achieve a full integration of technology to address complex care needs in PD.
Background
Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status.
Objective
The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes.
MethodsA systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components.
ResultsOur final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95% CI 475.89-1425.18; P
Background: Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient’s habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.
Methods: A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strat- egy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extrac- tion for the narrative review.
Results: Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm’s accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.
Conclusion: This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
Wearable inertial sensors can be used to monitor mobility in real-world settings over extended periods. Although these technologies are widely used in human movement research, they have not yet been qualified by drug regulatory agencies for their use in regulatory drug trials. This is because the first generation of these sensors was unreliable when used on slow-walking subjects. However, intense research in this area is now offering a new generation of algorithms to quantify Digital Mobility Outcomes so accurate they may be considered as biomarkers in regulatory drug trials. This perspective paper summarises the work in the Mobilise-D consortium around the regulatory qualification of the use of wearable sensors to quantify real-world mobility performance in patients affected by Parkinson’s Disease. The paper describes the qualification strategy and both the technical and clinical validation plans, which have recently received highly supportive qualification advice from the European Medicines Agency. The scope is to provide detailed guidance for the preparation of similar qualification submissions to broaden the use of real-world mobility assessment in regulatory drug trials.
Background:
Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models.
Methods:
This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models.
Results:
Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77.
Conclusions:
Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.
Trunk flexion represents a risk factor for the onset of low-back disorders, yet limited quantitative data exist regarding flexion exposures in actual working conditions. In this study, we evaluated the potential of using a single inertial measurement unit (IMU) to classify trunk flexion, in terms of amplitude, frequency, and duration, and assessed the influence of alternative time durations on exposure results. Twelve warehouse workers were monitored during two hours of an actual shift while wearing a single IMU on their low back. Trunk flexion data were reduced using exposure variation analysis integrated with recommended exposure thresholds. Workers spent 5.1% of their working time with trunk flexion of 30-60° and 2.3% with flexion of 60-90°. Depending on the level of acceptable error, relatively shorter monitoring periods (up to 50 min) might be sufficient to characterize trunk flexion exposures. Future work is needed, however, to determine if these results generalize to other postural exposures and tasks.
Aim:
The main purpose of the present study was to verify the feasibility of wearable inertial sensors (IMUs) in a clinical setting to screen gait and functional mobility in Italian older persons. In particular, we intended to verify the capability of IMUs to discriminate individuals with and without cognitive impairments and assess the existence of significant correlations between mobility parameters extracted by processing trunk accelerations and cognitive status.
Methods:
This is a cross-sectional study performed on 213 adults aged over 65 years (mean age 77.0 ± 5.4; 62% female) who underwent cognitive assessment (through Addenbrooke's Cognitive Examination Revised, ACE-R) instrumental gait analysis and the Timed Up and Go (TUG) test carried out using a wearable IMU located in the lower back.
Results:
Individuals with cognitive impairments exhibit a peculiar gait pattern, characterized by significant reduction of speed (- 34% vs. healthy individuals), stride length (- 28%), cadence (- 9%), and increase in double support duration (+ 11%). Slight, but significant changes in stance and swing phase duration were also detected. Poorer performances in presence of cognitive impairment were observed in terms of functional mobility as overall and sub-phase TUG times resulted significantly higher with respect to healthy individuals (overall time, + 38%, sub-phases times ranging from + 22 to + 34%), although with some difference associated with age. The severity of mobility alterations was found moderately to strongly correlated with the ACE-R score (Spearman's rho = 0.58 vs. gait speed, 0.54 vs. stride length, 0.66 vs. overall TUG time).
Conclusion:
The findings obtained in the present study suggest that wearable IMUs appear to be an effective solution for the clinical assessment of mobility parameters of older persons screened for cognitive impairments within a clinical setting. They may represent a useful tool for the clinician in verifying the effectiveness of interventions to alleviate the impact of mobility limitations on daily life in cognitively impaired individuals.
Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.
Background:
Sensor-based technological therapy devices may be good candidates for neuromotor rehabilitation of people with Multiple Sclerosis (MS), especially for treating upper extremities function limitations. The sensor-based device rehabilitation is characterized by interactive therapy games with audio-visual feedback that allows training the movement of shoulders, elbows, and wrist, measuring the strength and the active range of motion of upper limb, registering data in an electronic database to quantitatively monitoring measures and therapy progress.
Objective:
This study aimed to investigate the effects of sensor-based motor rehabilitation in add-on to the conventional neurorehabilitation, on increasing the upper limb functions of patients with MS.
Methods:
Thirty patients were enrolled in the study and randomly assigned to the experimental group and the control group. The training consisting of twelve sessions of upper limb training was compared with twelve sessions of upper limb sensory-motor training, without robotic support. Both rehabilitation programs were performed for 40 minutes three times a week, for 4 weeks, in addition to conventional therapy. All patients were evaluated at the baseline (T0) and after 4 weeks of training (T1).
Results:
The within-subject analysis showed a statistically significant improvement in both groups, in the Modified Barthel Index and in the Rivermead Mobility Index scores and a significant improvement in Multiple Sclerosis Quality of Life-54 in the experimental. The analysis of effectiveness revealed that, compared with baseline (T0), the improvement percentage in all clinical scale scores was greater in the experimental group than the control group.
Conclusions:
Proposed training provides an intensive and functional-oriented rehabilitation that objectively evaluates achieved progress through exercises. Therefore, it can represent a good complementary strategy for hand rehabilitation in MS patients.
BACKGROUND
Unhealthy behaviors such as physical inactivity, a sedentary lifestyle, and unhealthful eating remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain good health status.
OBJECTIVE
The objectives of this meta-analysis are: (i) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (e.g., steps, moderate-to-vigorous physical activity, weight), and (ii) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes.
METHODS
A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if: (i) they were randomized controlled trials (RCTs), (ii) the intervention involved the use of a Fitbit device, and (iii) the reported outcomes were related to healthy lifestyles. The main outcome measures are related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with the control group. We also conducted subgroup and fuzzy-set qualitative comparative analyses (fsQCA) to further disentangle the effects of intervention components.
RESULTS
Our final sample comprises 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 927.35; 95% CI 363.99 to 1,490.71; P=.001), and moderate and vigorous physical activity (MD 6.17; 95% CI 2.81 to 9.52; P<.001), a significant decrease in weight (MD -1.45; 95% CI -2.63 to -0.27; P=.02) and a nonsignificant decrease in objectively-assessed and self-reported sedentary behavior (MD -10.62; 95% CI -35.50 to 14.27; P=.40; SMD -0.11; 95% CI -0.48 to 0.26; P=.56, respectively). In general, included studies were at low risk for bias, except for performance bias. Subgroup analyses and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component.
CONCLUSIONS
The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support.
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body’s physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user’s learning activities and cognitive load.
Background:
Lack of physical activity (PA) is a risk factor for death and non-communicable disease. Despite this, more than one fourth of adults worldwide do not follow PA guidelines. As part of a feasibility study to test a complex intervention for increasing PA, we included a consumer-based activity tracker (AT) as a tool to measure PA outcomes and to track heart rate during exercise sessions. The aim of the present study was to identify factors that increase wear time when using a consumer-based AT for monitoring of participants in clinical research.
Methods:
Sixteen participants aged 55-74 years, with obesity, sedentary lifestyle, and elevated cardiovascular risk were recruited to a 12-month feasibility study. Participants wore a Polar M430 AT to collect continuous PA data during a six-month intervention followed by 6 months of follow-up. We performed quantitative wear time analysis, tested the validity of the AT, and completed two rounds of qualitative interviews to investigate how individual wear-time was linked to participant responses.
Results:
From 1 year of tracking, mean number of valid wear days were 292 (SD = 86), i.e. 80%. The Polar M430 provides acceptable measurements for total energy expenditure. Motivations for increased wear time were that participants were asked to wear it and the ability to track PA progress. Perceived usefulness included time keeping, heart rate- and sleep tracking, becoming more conscious about day-to-day activity, and improved understanding of which activity types were more effective for energy expenditure. Sources of AT annoyance were measurement inaccuracies and limited instruction for use. Suggestions for improvement were that the AT was big, unattractive, and complicated to use.
Conclusions:
Adherence to wearing a consumer-based AT was high. Results indicate that it is feasible to use a consumer-based AT to measure PA over a longer period. Potential success factors for increased wear time includes adequate instruction for AT use, allowing participants to choose different AT designs, and using trackers with accurate measurements. To identify accurate trackers, AT validation studies in the target cohort may be needed.
Trial registration:
U.S. National Library of Medicine, Clinical Trial registry: NCT03807323 ; Registered 16 September 2019 - Retrospectively registered.
The industrial societies face difficulty applying traditional work-related musculoskeletal disorder (WMSD) risk assessment methods in practical applications due to in-situ task dynamics, complex data processing, and the need of ergonomics professionals. This study aims to develop and validate a wearable inertial sensors-based automated system for assessing WMSD risks in the workspace conveniently, in order to enhance workspace safety and improve workers’ health. Both postural ergonomic analysis (RULA/REBA) and two-dimensional static biomechanical analysis were automatized as two toolboxes in the proposed system to provide comprehensive WMSD risk assessment based on the kinematic data acquired from wearable inertial sensors. The effectiveness of the developed system was validated through a follow-up experiment among 20 young subjects when performing representative tasks in the heavy industry. The RULA/REBA scores derived from our system achieved high consistency with experts’ ratings (intraclass correlation coefficient ≥0.83, classification accuracy >88%), and good agreement was also found between low-back compression force from the developed system and the reference system (mean intersystem coefficient of multiple correlation >0.89 and relative error <9.5%). These findings suggested that the wearable inertial sensors-based automated system could be effectively used for WMSD risk assessment of workers when performing tasks in the workspace.
Introduction
Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep.
Methods
Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms.
Discussion
The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity.
Trial Registration
UMIN000021396, University Hospital Medical Information Network (UMIN).
Dynamic motor imagery (dMI) is a motor imagery task associated with movements partially mimicking those mentally represented. As well as conventional motor imagery, dMI has been typically assessed by mental chronometry tasks. In this paper, an instrumented approach was proposed for quantifying the correspondence between upper and lower limb oscillatory movements performed on the spot during the dMI of walking vs. during actual walking. Magneto-inertial measurement units were used to measure limb swinging in three different groups: young adults, older adults and stroke patients. Participants were tested in four experimental conditions: (i) simple limb swinging; (ii) limb swinging while imagining to walk (dMI-task); (iii) mental chronometry task, without any movement (pure MI); (iv) actual level walking at comfortable speed. Limb swinging was characterized in terms of the angular velocity, frequency of oscillations and sinusoidal waveform. The dMI was effective at reproducing upper limb oscillations more similar to those occurring during walking for all the three groups, but some exceptions occurred for lower limbs. This finding could be related to the sensory feedback, stretch reflexes and ground reaction forces occurring for lower limbs and not for upper limbs during walking. In conclusion, the instrumented approach through wearable motion devices adds significant information to the current dMI approach, further supporting their applications in neurorehabilitation for monitoring imagery training protocols in patients with stroke.
Mobile applications provide the healthcare industry with a means of connecting with patients in their own home utilizing their own personal mobile devices such as tablets and phones. This allows therapists to monitor the progress of people under their care from a remote location and all with the added benefit that patients are familiar with their own mobile devices; thereby reducing the time required to train patients with the new technology. There is also the added benefit to the health service that there is no additional cost required to purchase devices for use. The Facial Remote Activity Monitoring Eyewear (FRAME) mobile application and web service framework has been designed to work on the IOS and android platforms, the two most commonly used today. Results: The system utilizes secure cloud based data storage to collect, analyse and store data, this allows for near real time, secure access remotely by therapists to monitor their patients and intervene when required. The underlying framework has been designed to be secure, anonymous and flexible to ensure compliance with the data protection act and the latest General Data Protection Regulation (GDPR); this new standard came into effect in April 2018 and replaces the Data Protection Act in the UK and Europe.
Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients’ functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson’s disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.
Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49-80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions.
Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.
Introduction
Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture.
Methods and analysis
Arksey and O’Malley’s methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps.
Ethics and dissemination
As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team.
Registration
Study materials and updates will be made available through the Center for Open Science’s OSFRegistry ( https://osf.io/k7395 ).
Background:
Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures.
Objective:
The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure.
Methods:
We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability.
Results:
We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.
Conclusions:
Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.
INTRODUCTION: This systematic review and meta-analysis aims at synthesising and evaluating studies that used wearable inertial sensors for assessing gait-related kinematic variables in total knee arthroplasty (TKA) interventions.
EVIDENCE ACQUISITION: PubMed/MEDLINE, EMBASE, Scopus, and PEDro databases were searched from inception to December 1, 2020.
EVIDENCE SYNTHESIS: This study was conducted in agreement with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Three reviewers assessed studies for inclusion and extraction data. All studies that performed analysis with inertial measurement units (IMUs) before and after TKA have been included for the meta-analysis and the effect sizes and 95% confidence interval (CI) were calculated by random-effect models. Egger regression and the Begg-Mazumdar Rank Correlation Test were used to assess publication bias. A total of 7 studies involving 143 patients subjected to TKA met the inclusion criteria for the meta-analysis. The use of IMUs in the assessment of spatio-temporal parameters of gait after TKA showed a significant pooled effect size (P<0.05) in the assessment of gait speed, step frequency, step length, and step duration. High statistical heterogeneity across studies was detected for step frequency and duration, while moderate and low heterogeneity was observed for gait speed and step length, respectively.
CONCLUSIONS: The findings of the present review support the feasibility of using IMUs in detecting changes in spatio-temporal parameters of gait after one year from TKA surgery. Specifically, step length and gait speed seem to be the most sensitive parameters for discriminating changes in gait performance. No sufficient data are available to recommend the use of other gait-related kinematic variables.
KEY WORDS: Arthroplasty, replacement, knee; Knee prosthesis; Gait analysis; Wearable electronic devices
As the use of remote patient monitoring services grows — driven by health care limitations imposed by the Covid-19 pandemic — clinicians, payers, and patients face important questions regarding the volume, value, and appropriate use of this care model.
Motor imagery is a mental process not accompanied by movement and widely studied in healthy subjects, related to hand movements in terms of timing. This study compared static and dynamic motor imagery analyzing temporal and spatial features in different locomotor conditions in three different groups of subjects: high-skilled athletes with visual impairments, a group of sighted unprofessional athletes and a control group of sighted subjects. We found that dynamic motor imagery resulted in timely closer to real performance than static motor imagery. The discrepancies between dynamic motor imagery and real condition, in fact, resulted limited to uncommon locomotion, such as lateral walking. Motor imagery resulted closer to real performance in terms of timing than in terms of step length, with the exception of athletes with visual impairments that, differently from the other groups, did not show any significant differences between the numbers of imagined and performed steps. It opens a new question about the relationship between temporal and spatial imagination of locomotion.
There is a growing body of literature about the efficacy in neurorehabilitation of the devices providing rhythmic auditory stimulations or visual-auditory stimulations, such as videogames, for guiding the patients' movements. Despite being presented as tools able to motivate patients, their efficacy was not been proven yet, probably due to the limited knowledge about the factors influencing the capability of patients to move the upper limbs following an external stimulus. In this study, we used a marker less system based on two infrared sensors to assess the kinematics of up and down in-phase and anti-phase bilateral hand oscillations synchronized or not with an external stimulus. A group of stroke survivors, one of age-matched healthy subjects and one of young healthy subjects were tested in three conditions: no stimulus, auditory stimulus, and video-auditory stimulus. Our results showed significant negative effects of visual-auditory stimulus in the frequency of movements (p = 0.001), and of auditory stimulus in their fluidity (p = 0.013). These results are conceivably related to the attentional overload required during the execution of bilateral movements driven by an external stimulus. However, a positive effect of external stimulus was found in increasing the range of movements of the less functional hand in all subjects (p = 0.023). These findings highlight as the type of stimulus may play a crucial role in the patient's performance with respect to movements that are not-externally driven.
Aim
To investigate the efficacy of a virtual reality rehabilitation system of wearable multi‐inertial sensors to improve upper‐limb function in children with brain injury.
Method
Eighty children (39 males, 41 females) with brain injury including cerebral palsy aged 3 to 16 years (mean age 5y 8mo, SD 2y 10mo) were assessed as part of a multicentre, single‐blind, randomized controlled trial. The intervention group received a 30‐minute virtual reality intervention and a 30‐minute session of conventional occupational therapy while the control group received 60 minutes of conventional occupational therapy per session, with 20 sessions over 4 weeks. The virtual reality rehabilitation system consisted of games promoting wrist and forearm articular movements using wearable inertial sensors. The Melbourne Assessment of Unilateral Upper Limb Function‐2 (MA‐2), Upper Limb Physician’s Rating Scale, Pediatric Evaluation of Disability Inventory Computer Adaptive Test, and computerized three‐dimensional motion analysis were performed.
Results
Both groups (virtual reality, n=40; control, n=38) significantly improved after treatment compared to baseline; however, the virtual reality group showed more significant improvements in upper‐limb dexterity functions (MA‐2, virtual reality group: Δ=10.09±10.50; control: Δ=3.65±6.92), performance of activities of daily living, and forearm supination by kinematic analysis (p<0.05). In the virtual reality group, children with more severe motor impairment showed significant improvements compared to those with less severe impairment.
Interpretation
The virtual reality rehabilitation system used in this study, which consists of wearable inertial sensors and offers intensive, interactive, and repetitive motor training, is effective in children with brain injury.
Fall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to provide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, on-wrist devices) to measure the daily activity strongly suggests that in a short period of time, the elderly people will be making use of this type of devices. On-wrist devices can be used as the FD basic sensing unit; while the intelligent classification can be obtained either autonomously (on the device) or requested to a remote service (via the paired smartphone or via web services). This study tries to analyze the current challenges in autonomous on-wrist wearable devices for producing a reliable and robust FD system. To do so, we analyze the related work; one of the possible solutions is implemented with several alternatives and evaluated with publicly available simulated falls data sets. The most remarkable findings in this research are that i) real fall data sets are needed, at least, a valid merging method to produce real fall like Time Series, ii) generalized solutions might not be enough and research is needed in models that learns from the user, iii) the need of tuning and fitting to the current user performance, iv) the amount of fall types suggests that hybrid and ensemble approaches might be interesting.
The ever-growing threats of security and privacy loss from unauthorized access to mobile devices have led to the development of various biometric authentication methods for easier and safer data access. Gait-based authentication is a popular biometric authentication as it utilizes the unique patterns of human locomotion and it requires little cooperation from the user. Existing gait-based biometric authentication methods however suffer from degraded performance when using mobile devices such as smart phones as the sensing device, due to multiple reasons, such as increased accelerometer noise, sensor orientation and positioning, and noise from body movements not related to gait. To address these drawbacks, some researchers have adopted methods that fuse information from multiple accelerometer sensors mounted on the human body at different locations. In this work we present a novel gait-based continuous authentication method by applying multimodal learning on jointly recorded accelerometer and ground contact force data from smart wearable devices. Gait cycles are extracted as a basic authentication element, that can continuously authenticate a user. We use a network of auto-encoders with early or late sensor fusion for feature extraction and SVM and softmax for classification. The effectiveness of the proposed approach has been demonstrated through extensive experiments on datasets collected from two case studies, one with commercial off-the-shelf smart socks and the other with a medical-grade research prototype of smart shoes. The evaluation shows that the proposed approach can achieve a very low Equal Error Rate of 0.01% and 0.16% for identification with smart socks and smart shoes respectively, and a False Acceptance Rate of 0.54%-1.96% for leave-one-out authentication.
Manual load carrying without sufficient rest may cause work-related musculoskeletal disorders (WMSDs) and needs to be monitored at construction sites. While previous studies have been able to predict load-carrying modes using multiple wearable inertial measurement unit (IMU) sensors, wearing multiple sensors obtrudes on workers during various construction tasks. In this context, by using a single IMU sensor, this research proposes an automatic detecting technique for excessive carrying-load (DeTECLoad) to predict load-carrying weights and postures simultaneously. DeTECLoad converts the IMU data into image data using a Gramian Angular Field, and then uses a hybrid Convolutional Neural Network-Long Short-Term Memory to classify load-carrying modes from the image data. DeTECLoad provides 92.46% and 96.33% accuracies for the load-carrying weight and posture classifications, respectively. By exploiting DeTECLoad, a construction worker's excessive load-carrying tasks could be managed in situ, helping to prevent construction site WMSDs.
The goal of this paper is to shed some light on the usefulness of a contact tracing smartphone app for the containment of the COVID-19 pandemic. We review the basics of contact tracing during the spread of a virus, we contextualize the numbers to the case of COVID-19 and we analyze the state of the art for proximity detection using Bluetooth Low Energy. Our contribution is to assess if there is scientific evidence of the benefit of a contact tracing app in slowing down the spread of the virus using present technologies. Our conclusion is that such evidence is lacking, and we should re-think the introduction of such a privacy-invasive measure.
BACKGROUND
Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population.
OBJECTIVE
The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible.
METHODS
From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty.
RESULTS
A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older ( P <.01), utilized more home care service ( P =.01), walked less ( P =.04), slept longer ( P =.01), and had longer deep sleep time ( P <.01). Total sleep time (r=0.41, P =.01) and deep sleep time (r=0.53, P <.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P =.88).
CONCLUSIONS
We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services.