ArticleLiterature Review

Wearable inertial sensors for human movement analysis: a five-year update

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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.

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... Next, participants were equipped with a lumbar belt with the Android device fixed horizontally and parallel to the ground so that the upper edge was aligned with the joining of the posterior-superior iliac spines (Fig. 1). The positioning and fixation of the device in correspondence with the center of mass (COM) of the whole body, by means of an elastic belt able to secure it and to avoid any displacement, was in line with previous literature on the instrumental assessment of walking ability and stability in patients with stroke performed with a single wearable device [50][51][52][53][54][55][56][57]. The validity of this approach with specially developed wearable devices fixed with an elastic belt had already been demonstrated, but the use of a smartphone, which incorporates an inertial unit and could allow for a more widespread use of this technique, had not been tested. ...
... Thus, new motion analysis devices are being developed that are smaller and lighter with more data storage space and less time-consuming, and are found to be an alternative to measure patterns of movement in clinical settings [90]. The latest generation of smartphones often incorporates micro-electromechanical inertial systems with accelerometers and gyroscopes, endowing them with an enormous potential for monitoring the parameters of human movement [56,89]. In addition, the large onboard memory capacity and wireless connectivity for data transfer make modern smartphones ideal candidates for remote health monitoring [56]. ...
... The latest generation of smartphones often incorporates micro-electromechanical inertial systems with accelerometers and gyroscopes, endowing them with an enormous potential for monitoring the parameters of human movement [56,89]. In addition, the large onboard memory capacity and wireless connectivity for data transfer make modern smartphones ideal candidates for remote health monitoring [56]. Current evidence demonstrates that both classic clinical tests and instrumental measurements are required when assessing balance after stroke, leading to development of a better individualized treatment program [5]. ...
Article
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Background Incorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis. Methods This is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants’ kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied. Results Compared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively). Conclusion This study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.
... Therefore, gait assessment using video cameras and pose estimation has limitations for assessment in daily life. Cadence and step time variability of gait differ between laboratory and daily-life assessments [12], and gait analysis in daily life is expected to become more important for health care and health promotion [13]. ...
... Wearable sensors can be another alternative method to evaluate gait kinematics in daily life and are increasingly being used for the evaluation of lower extremity kinematics [13]. The assessment using these sensors can be conducted anywhere and is not limited to a space, and the data are easier to record and analyze than traditional optical motion capture systems [13]. ...
... Wearable sensors can be another alternative method to evaluate gait kinematics in daily life and are increasingly being used for the evaluation of lower extremity kinematics [13]. The assessment using these sensors can be conducted anywhere and is not limited to a space, and the data are easier to record and analyze than traditional optical motion capture systems [13]. Inertial measurement units (IMUs) with accelerometers and gyroscopes are one of the most commonly used wearable systems for kinematic analysis. ...
Article
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Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual’s body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5–3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9–13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67–82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.
... In the Stroop task the subject is asked to verbally read a word, that is the name of a colour, that could be written in the same colour semantically represented by that word (congruent condition) or in another colour (incongruent condition). It is a widely used cognitive test assessing the ability to regulate thoughts and actions in accordance with internally maintained behavioral goals, by the activation of a cognitive control [7]. 2 In the last decades, there has been an increasing diffusion of wearable devices allowing to measure cardiac functions, electrodermal activity, skin temperature and electromyographic activity and also kinematic parameters of trunk movements thanks to embedded inertial sensors [8,9]. More recently, researchers tried to use these wearable devices to investigate the complex relationships between cardiac functions, cognitive aspects and control of movements [10]. ...
... The aim of this study is to propose a simple protocol to identify the principal components of this complex bidirectional system integrating heart, motor control and cognitive functions. According to the literature this protocol is based on the analysis of HRV and its relationship to the results of Stroop task, adding the analysis of trunk rotations, performed using a wearable inertial unit containing a triaxial accelerometer, a triaxial gyroscope and a magnetometer for the measure of the range of motion [8,9]. From a bioengineering point of view a device including inertial sensors for analyzing trunk movements and electrodes recording the cardiac signal for computing the heart rate variability has been proposed. ...
... De Bartolo and colleagues claimed the need of evaluation methods for a quantitative assessment of cardiac, cognitive and motor interactions that could be helpful in physiological research, sport training of athletes and, more specifically, for rehabilitation purposes [10]. In fact, neurorehabilitation may benefit of an integrated approach not aiming at the separate recovery of specific single functions, 9 but curing and caring the patient as a whole person [34]. Even more, the investigation of this connection and of the brain's sense of movement are particularly interesting in developmental age [35] when the trunk mental representation could be altered affecting the symmetry of posture and movements [36]. ...
Preprint
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Some studies investigated the relationship between frequency domain analysis of heart rate variability and the cognitive performance at the Stroop task. We propose a combined assessment also of trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac and motor variables based on a principal component analysis. We also assessed the change on this result after an intervention of two months aiming at improving the perception-action link. At baseline, the PCA correctly identified 3 components: one related to cardiac variables, one to trunk motion and one to the performance of Stroop task. After the intervention, only two components were found with trunk symmetry and range of motion, accuracy and time to complete the Stroop task and low frequency heart rate variability aggregated in a single component by PCA. It suggested that this protocol was effective in investigating the embodied cognition and we defined this approach as “embodimetrics”.
... Sensors enable the creation of a comprehensive behavioral profile generated from continuous monitoring over the long-term [9]. These sensors encompass a variety of devices, including wearables [31,59], smart home devices [70,72], and cameras [36,69], among others. They have demonstrated their effectiveness in various healthcare applications. ...
... Wearable sensors have emerged as valuable tools for measuring healthcare-related parameters in older adults. These sensors accurately measure human motion, localization, and tracking, making them suitable for various applications, such as frailty assessment, fall risk evaluation, monitoring chronic neurological diseases, promoting active living, and cognitive assessment [9,59]. A study demonstrated that inertial sensors effectively assess frailty, providing an objective measure of an individual's physical condition [57]. ...
Preprint
Aging and chronic conditions affect older adults' daily lives, making early detection of developing health issues crucial. Weakness, common in many conditions, alters physical movements and daily activities subtly. However, detecting such changes can be challenging due to their subtle and gradual nature. To address this, we employ a non-intrusive camera sensor to monitor individuals' daily sitting and relaxing activities for signs of weakness. We simulate weakness in healthy subjects by having them perform physical exercise and observing the behavioral changes in their daily activities before and after workouts. The proposed system captures fine-grained features related to body motion, inactivity, and environmental context in real-time while prioritizing privacy. A Bayesian Network is used to model the relationships between features, activities, and health conditions. We aim to identify specific features and activities that indicate such changes and determine the most suitable time scale for observing the change. Results show 0.97 accuracy in distinguishing simulated weakness at the daily level. Fine-grained behavioral features, including non-dominant upper body motion speed and scale, and inactivity distribution, along with a 300-second window, are found most effective. However, individual-specific models are recommended as no universal set of optimal features and activities was identified across all participants.
... In recent decades, there has been an increasing proliferation of wearable devices capable of measuring cardiac functions, electrodermal activity, skin temperature, electromyographic activity, and kinematic parameters of trunk movements, thanks to embedded inertial sensors [8,9]. More recently, researchers have attempted to utilize these wearable devices to investigate the complex relationships between cardiac functions, cognitive aspects, and movement control [10]. ...
... It has been reported that an increase in trunk mobility may alter the sympathovagal balance, thereby modifying HRV [15]. Trunk rotations were measured using a wearable inertial unit containing a triaxial accelerometer, a triaxial gyroscope, and a magnetometer (used for measuring the range of motion) [8,9]. From a bioengineering perspective, a device embedding inertial sensors for analyzing trunk movements and electrodes for recording cardiac signals to compute heart rate variability was proposed. ...
Article
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There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac, and motor variables using principal component analysis (PCA). Additionally, we assessed changes in the relationships among these variables after a two-month intervention aimed at improving the perception–action link. At baseline, PCA correctly identified three components: one related to cardiac variables, one to trunk motion, and one to Stroop task performance. After the intervention, only two components were found, with trunk symmetry and range of motion, accuracy, time to complete the Stroop task, and low-frequency heart rate variability aggregated into a single component using PCA. Artificial neural network analysis confirmed the effects of both HRV and motor behavior on cognitive Stroop task performance. This analysis suggested that this protocol was effective in investigating embodied cognition, and we defined this approach as “embodimetrics”.
... A common shortcoming of these functional tests is the lack of ecological validity: Walking, as measured in clinical settings, does not reflect daily life walking (3,(10)(11)(12). The transition to unsupervised monitoring of human motion in naturalistic and unconstrained daily life activities is driven mainly using wearable inertial measurement units (IMUs) (4,13). It is noteworthy that meanwhile both European and American notified bodies for the certification of medical devices (Medical Device Regulation and Food and Drug Administration, respectively) have put focus on wearable sensors by updating their regulations for the design, pre-clinical validation, and clinical validation of devices that include wearable IMUs (13,14). ...
... The transition to unsupervised monitoring of human motion in naturalistic and unconstrained daily life activities is driven mainly using wearable inertial measurement units (IMUs) (4,13). It is noteworthy that meanwhile both European and American notified bodies for the certification of medical devices (Medical Device Regulation and Food and Drug Administration, respectively) have put focus on wearable sensors by updating their regulations for the design, pre-clinical validation, and clinical validation of devices that include wearable IMUs (13,14). Similarly, both the European Medicines Agency and the United States Food and Drug Administration encourage the inclusion of parameters from unsupervised patient monitoring as exploratory endpoints in clinical trials (11,15). ...
Article
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Introduction The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.
... This assumption is partially supported by the literature reviewed in our article [17,18,21]. However, considering the complexity of fall detection, hybrid systems made by wearable and non-wearable systems are hypothesized and proposed [27]. ...
... The next few years will be essential to translate all the advances that the engineering, information technology and biomedical fields have made to improve the prevention of falls at home. In fact, we expect an improvement in garment, wearable and nonwearable technological devices that will become more usable, cheaper, and therefore more widespread [27]. Moreover, the adopted technology must increasingly undergo a technical validation procedure when aiming its use in real-world scenarios [30]. ...
Article
Introduction: Monitoring systems at home are critical in the event of a fall, and can range from standalone fall detection devices to activity recognition devices that aim to identify behaviors in which the user may be at risk of falling, or to detect falls in real-time and alert emergency personnel. Areas covered: This review analyzes the current literature concerning the different devices available for home fall detection. Expert opinion: Included studies highlight how fall detection at home is an important challenge both from a clinical-assistance point of view and from a technical-bioengineering point of view. There are wearable, non-wearable and hybrid systems that aim to detect falls that occur in the patient's home. In the near future, a greater probability of predicting falls is expected thanks to an improvement in technologies together with the prediction ability of machine learning algorithms. Fall prevention must involve the clinician with a person-centered approach, low cost and minimally invasive technologies able to evaluate the movement of patients and machine learning algorithms able to make an accurate prediction of the fall event.
... The survey also sheds light on the primary use of technological devices in managing neurological conditions [17][18][19][20][21], particularly in enhancing functions and activities within the scope of the International Classification of Functioning, Disability and Health (ICF), focusing on balance and dexterity. Given the frequent occurrence of balance issues in both musculoskeletal and neurological rehabilitation, technology targeting these problems is likely among the earliest to be extensively available and researched. ...
Article
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Introduction In the evolving healthcare landscape, technology has emerged as a key component in enhancing system efficiency and offering new avenues for patient rehabilitation. Despite its growing importance, detailed information on technology's specific use, types, and applications in clinical rehabilitation settings, particularly within the Italian framework, remains unclear. This study aimed to explore the use of technology and its needs by Physical Medicine and Rehabilitation medical doctors in Italy. Methods We conducted a cross-sectional online survey aimed at 186 Italian clinicians affiliated with the Italian Society of Physical and Rehabilitation Medicine (SIMFER). The online questionnaire consists of 71 structured questions designed to collect demographic and geographical data of the respondents, as well as detailed insights into the prevalence and range of technologies they use, together with their specific applications in clinical settings." Results A broad range of technologies, predominantly commercial medical devices, has been documented. These technologies are employed for various conditions, including common neurological diseases, musculoskeletal disorders, dementia, and rheumatologic issues. The application of these technologies indicates a broadening scope beyond enhancing sensorimotor functions, addressing both physical and social aspects of patient care. Discussion In recent years, there's been a notable surge in using technology for rehabilitation across various disorders. The upcoming challenge is to update health policies to integrate these technologies better, aiming to extend their benefits to a wider range of disabling conditions, marking a progressive shift in public health and rehabilitation practices.
... Initially, IMUs were limited primarily to basic locomotive outcomes, such as spatiotemporal and accelerometeronly outcomes. Advancements in sensor technology, signal processing methods, and data fusion techniques have broadened the capabilities of IMUs [15]. Sensor data can be integrated with biomechanical models and machine learning techniques; to estimate a variety of kinematic and kinetic measures, which can be obtained in more ecologically valid settings in real-time, and over extended durations [16][17][18]. ...
Article
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Background: Fatigue manifests as a decline in performance during high-intensity and prolonged exercise. With technological advancements and the increasing adoption of inertial measurement units (IMUs) in sports biomechanics, there is an opportunity to enhance our understanding of running-related fatigue beyond controlled laboratory environments. Research question: How have IMUs have been used to assess running biomechanics under fatiguing conditions? Methods: Following the PRISMA-ScR guidelines, our literature search covered six databases without date restrictions until September 2024. The Population, Concept, and Context criteria were used: Population (distance runners ranging from novice to competitive), Concept (fatigue induced by running a distance over 400 m), Context (assessment of fatigue using accelerometer, gyroscope, and/or magnetometer wearable devices). Biomechanical outcomes were extracted and synthesised, and interpreted in the context of three main study characteristics (cohort ability, testing environment, and the inclusion of physiological outcomes) to explore their potential role in influencing outcomes. Results: A total of 88 articles were included in the review. There was a high prevalence of treadmill-based studies (n=46, 52%), utilising only 1-2 sensors (n=69, 78%), and cohorts ranged in experience, from sedentary to elite-level runners, and were largely comprised of males (69% of all participants). The majority of biomechanical outcomes assessed showed varying responses to fatigue across studies, likely attributable to individual variability, exercise intensity, and differences in fatigue protocol settings and prescriptions. Spatiotemporal outcomes such as stride time and frequency (n=37, 42 %) and impact accelerations (n=55, 62%) were more widely assessed, with a fatigue response that appeared population and environment specific. Significance: There was notable heterogeneity in the IMU-based biomechanical outcomes and methods evaluated in this review. The review findings emphasise the need for standardisation of IMU-based outcomes and fatigue protocols to promote Interpretable metrics and facilitate inter-study comparisons
... Although several measurement tools have been developed to assess gait impairment in PwS, they are designed to capture performance or functional disability rather than a more in-depth quantification of the cardinal signs of gait impairment, such as asymmetry or trunk impairment [39]. Inertial measurement units (IMU) are a viable option for motion analysis in PwS due to the affordability and adaptability to a variety of clinical settings [40]. IMUs can monitor the body's center of mass while moving the base of support, resulting in effective tools for monitoring dynamic balance during gait [39,41,42]. ...
Article
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Background: Stroke-induced immunosuppression (SII) represents a negative rehabilitative prognostic factor associated with poor motor performance at discharge from a neurorehabilitation unit (NRB). This study aims to evaluate the association between SII and gait impairment at NRB admission. Methods: Forty-six stroke patients (65.4 ± 15.8 years, 28 males) and 42 healthy subjects (HS), matched for age, sex, and gait speed, underwent gait analysis using an inertial measurement unit at the lumbar level. Stroke patients were divided into two groups: (i) the SII group was defined using a neutrophil-to-lymphocyte ratio ≥ 5, and (ii) the immunocompetent (IC) group. Harmonic ratio (HR) and short-term largest Lyapunov’s exponent (sLLE) were calculated as measures of gait symmetry and stability, respectively. Results: Out of 46 patients, 14 (30.4%) had SII. HR was higher in HS when compared to SII and IC groups (p < 0.01). HR values were lower in SII when compared to IC subjects (p < 0.01). sLLE was lower in HS when compared to SII and IC groups in the vertical and medio-lateral planes (p ≤ 0.01 for all comparisons). sLLE in the medio-lateral plane was higher in SII when compared to IC subjects (p = 0.04). Conclusions: SII individuals are characterized by a pronounced asymmetric gait and a more impaired dynamic gait stability. Our findings underline the importance of devising tailored rehabilitation programs in patients with SII. Further studies are needed to assess the long-term outcomes and the role of other clinical features on gait pattern.
... In the case of athletes, they made it possible to observe athletic gestures, providing important information in the prevention of injuries and/or for the development of new materials and equipment, as well as providing feedback on performance that could help both athletes and coaches to improve performance. In the clinical field, they have proved particularly useful in the assessment of tremor, providing objective information in terms of frequency and intensity of oscillations [31,32]; in the assessment of people at risk of falling [30]; and in orthopedics, where they have been used to assess the change in walking pattern in people with hip and knee replacements; and in the study of neurological disorders such as stroke outcomes [33], Parkinson's Disease (PD) [13], cerebellar ataxia, in Multiple Sclerosis (MS), in patients with foot drop [32] or in cases of infantile cerebral palsy [32]. Several studies have already demonstrated the potential of using IMUs in patients with MS, as they provide quantitative information on the main gait alterations and allow discrimination between different walking abilities [34]. ...
Chapter
Dynamic postural stability is a complex multifactorial system in which motor, sensory, and cognitive components interact. This information is integrated by the central nervous system in a continuous sensory reweighting that ensures postural control in both static and dynamic conditions. This chapter analyzes the state of the art of instrumented assessment of dynamic stability in the main neurological pathologies such as stroke, Parkinson’s disease, cerebellar ataxia, and cerebral palsy. Finally, the translational implications in the field of neurorehabilitation and future perspectives are discussed.
... The necessary steps towards acceptance and adoption of novel digital endpoints include the technical and analytical validation for accuracy and reliability, as well as, patient experience, clinical validation, and clinical meaningfulness [26][27][28][29][30] . While the analytical validation of gait and activity endpoints are generally performed during a controlled in-lab environment, where traditional and reference methods can be measured simultaneously with the test methods for comparison, the validation of these endpoints during longer, naturalistic, and free living conditions has been more challenging 7,31 . Nonetheless, multiple DHT validation studies have shown evidence of measuring reliable gait and activity related metrics in both pediatric and adult populations [32][33][34][35][36][37][38][39][40][41][42] . ...
Article
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Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we designed a master protocol to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants’ self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home.
... Portable smart devices integrating motion sensors and physiological monitoring have shown broad application prospects in the health care, particularly in improving self-management for patients with chronic diseases (56)(57)(58). For patients with PD at a high risk of falls, wearable devices offer new solutions for fall prevention through convenient monitoring and data collection capabilities (59)(60)(61). ...
Article
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Background Recent years have witnessed a rapid growth in research on accidental falls in patients with Parkinson’s Disease (PD). However, a comprehensive and systematic bibliometric analysis is still lacking. This study aims to systematically analyze the current status and development trends of research related to accidental falls in patients with PD using bibliometric methods. Methods We retrieved literature related to accidental falls in patients with PD published between January 1, 2003, and December 31, 2023, from the Web of Science Core Collection (WoSCC) database. Statistical analysis and knowledge mapping of the literature were conducted using VOSviewer, CiteSpace, and Microsoft Excel software. Results A total of 3,195 publications related to accidental falls in patients with PD were retrieved. These articles were authored by 13,202 researchers from 3,834 institutions across 87 countries and published in 200 academic journals. Over the past 20 years, the number of published articles and citations has increased annually. The United States and the United Kingdom have the highest number of publications in this field, while Harvard University and Tel Aviv University are the most influential institutions. The Parkinsonism & Related Disorders journal published the highest number of articles, while the Movement Disorders journal had the highest number of citations. The most prolific author is Bloem, Bastiaan R, while the most cited author is Hausdorff, Jeffrey. The main research areas of these publications are Neurosciences, Biomedical, Electrical & Electronic, and Biochemistry & Molecular Biology. Currently, high-frequency keywords related to accidental falls in patients with PD include risk factors, clinical manifestations, and interventions. Prediction and prevention of accidental falls in such patients is a research topic with significant potential and is currently a major focus of research. Conclusion This study used bibliometric and knowledge mapping analysis to reveal the current research status and hotspots in the field of accidental falls in patients with PD. It also points out directions for future research. This study can provide theoretical support and practical guidance for scholars to further conduct related research.
... However, inertial sensors have some limitations. Despite being light and small, these devices may not be entirely transparent for users, especially due to the high number of sensors that, in many cases, must be used to obtain data that accurately interpret human movement [30,38,39]. ...
Article
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Background: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual’s functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity. Objective: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals. Methods: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition. Results: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs. Conclusions: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization—being noninvasive and practically imperceptible to the user—which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.
... To meet these expectations, wearable sensors must be benchmarked against clinical standards, provide reliable data over extended periods of time, be robust to placement errors by nonexperts, and offer tangible translational potential. Inertial sensing is one of the only wearable technologies that have been comprehensively characterized and benchmarked against gold-standard biomechanical measurements, but it remains sensitive to both drift and placement error [59,114,115] and does not capture muscle activity, which is relevant to both mobility impairments and the development of assistive wearables. Here, we investigate capacitive sensing [116] as a muscle-activity monitoring technology, finding for the first time that it can track changes in muscle bulging and fiber length with the fidelity of laboratory tools, in natural environments, over long durations, on multiple body parts, and on people of varying body compositions. ...
Thesis
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Biomechanical analysis and musculoskeletal simulation techniques have been developed in laboratory settings to help us understand the body and the mechanisms of motion with enough precision to evaluate health and suggest rehabilitative treatments that aim to improve musculoskeletal function. To achieve this same result outside of a laboratory, portable sensors must be made available that can monitor the motion of the body, kinematics, the contact interactions between the body and the surrounding environment, kinetics, and the forces generated by the body that enable locomotion, muscle dynamics. While many portable sensing options have been developed in recent years, the accuracy of portable biomechanics monitoring techniques has yet to match the accuracy of traditional laboratory-based tools and remains insufficient for many rehabilitative applications. One cause for insufficient accuracy is that portable sensing techniques are often explored in isolation and unable to overcome their unique limitations. Another cause is that many possible alternative portable sensing approaches developed outside of the biomechanics field have yet to be fully investigated for their potential to serve as biomechanics monitoring tools in combination with existing portable techniques. Here, I show how developments in inertial sensing and computer vision techniques can be intelligently synthesized using either kinematics equations of motion or rigid body dynamics equations of motion to enable more accurate portable predictions of body kinematics than approaches which utilize only inertial sensors or computer vision (Chapter 2). I also show how nuance exists in the choice of fusion approach depending on the quality of inertial sensing data and computer vision estimates. A prominent trade-off exists when adding rigid body dynamics into the synthesis paradigm, and adding dynamics is helpful so long as the dynamics equations provide more accurate estimates of angular velocities and accelerations than inertial sensing data, which is likely to occur during real-world applications due to soft-tissue motion artifacts. Next, I show how capacitive sensing, a sensing technique that has been understudied in biomechanics, can be adapted for use as a customizable, comfortable, lightweight, and sensitive biomechanics monitoring wearable sensor that enables muscle-activity measurements with the fidelity of gold-standard laboratory-based techniques (Chapter 3). Capacitive sensing muscle-activity measurements can then be synthesized with inertial sensors to enable full-body kinematics, kinetics, and muscle dynamics predictions with comparable accuracy to that of marker-based motion capture. Altogether, these findings show the importance of extensively validating and incorporating new sensing approaches into biomechanics monitoring tools that seamlessly integrate with other sensors to cover their weaknesses. I suggest that future biomechanics monitoring emphasize more nuanced applications, where multiple sensing modalities are fused intelligently and optimized for specific applications to maximize monitoring accuracy and intervention efficacy in each local domain, rather than sacrificing local accuracy to reach for a one-size-fits-all solution. In the future, I envision the development of a list of locally optimized biomechanics monitoring best practices, where specific sensor combinations with precise placements and parameterized computational algorithms are tuned to maximize monitoring accuracy for use on specific clinical populations and pathologies. I believe this more nuanced approach to biomechanics monitoring will enable the development of the next generation of rehabilitative strategies to improve and sustain more widespread musculoskeletal well-being.
... Smartphones are equipped with what are known as Inertial Measurement Units (IMUs), which consist of two inertial sensors: the accelerometer and the gyroscope. These sensors are tiny marvels of microelectronics, and their basic principle is to identify and quantify changes in the state of inertia of the smartphone (Picerno et al., 2021). Measurements of changes in inertia are indirect and are achieved by measuring acceleration using accelerometers and angular velocity using gyroscopes. ...
Chapter
The availability of technologies for assessing people's health is a limiting factor in many countries, especially the poorest ones. Smartphones offer a range of tools that can be useful for extracting biological signals that may be related to individuals' health conditions or diseases. Among these tools, inertial sensors and touchscreens enable the performance of motor tests that scientific literature has shown to be valid for the physical assessment of individuals. The integration of smartphones into public policies aimed at increasing health monitoring of individuals would allow for the expansion of the scope of quality assessment and preventive actions against functional declines.
... The survey also sheds light on the primary use of technological devices in managing neurological conditions (14)(15)(16)(17)(18), particularly in enhancing functions and activities within the scope of the International Classi cation of Functioning, Disability and Health (ICF), focusing on balance and dexterity. Given the frequent occurrence of balance issues in both musculoskeletal and neurological rehabilitation, technology targeting these problems is likely among the earliest to be extensively available and researched. ...
Preprint
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Introduction: In the evolving healthcare landscape, technology has emerged as a key component in enhancing system efficiency and offering new avenues for patient rehabilitation. Despite its growing importance, detailed information on technology's specific use, types, and applications in clinical rehabilitation settings, particularly within the Italian framework, remains unclear. This study aimed to explore the use of technology and its needs by Physical Medicine and Rehabilitation medical doctors in Italy. Methods: We conducted a cross-sectional online survey aimed at 186 Italian clinicians affiliated with the Italian Society of Physical and Rehabilitation Medicine (SIMFER). The online questionnaire consists of 71 structured questions designed to collect demographic and geographical data of the respondents, as well as detailed insights into the prevalence and range of technologies they use, together with their specific applications in clinical settings." Results: A broad range of technologies, predominantly commercial medical devices, has been documented. These technologies are employed for various conditions, including common neurological diseases, musculoskeletal disorders, dementia, and rheumatologic issues. The application of these technologies indicates a broadening scope beyond enhancing sensorimotor functions, addressing both physical and social aspects of patient care. Discussion: In recent years, there's been a notable surge in using technology for rehabilitation across various disorders. The upcoming challenge is to update health policies to integrate these technologies better, aiming to extend their benefits to a wider range of disabling conditions, marking a progressive shift in public health and rehabilitation practices.
... Sophisticated setups with advanced measurement systems provide accurate pose estimation [4], however, their use is limited to laboratory settings or professional applications. On the other hand, low-cost IMUs have become popular [5], while at the same time, the latest developments in deep learning allow obtaining accurate pose estimation from RGB videos using built-in cameras on mobile devices [6]. Combining low-cost IMUs and mobile cameras has the potential for making multi-modal motion tracking available for a wide range of users. ...
... The use of AI in the diagnosis and treatment of movement disorders has become increasingly prevalent. Wearable sensors and motion capture technology have been successfully employed to observe the advancement of these maladies and provide clinicians with vital information concerning the efficacy of treatments [19], [20]. AI has shown potential for delivering precise and trustworthy diagnoses in [21], [22] as well. ...
... The necessary steps towards acceptance and adoption of novel digital endpoints include the technical and analytical validation for accuracy and reliability, as well as, patient experience, clinical validation, and clinical meaningfulness [19][20][21][22][23] . While the analytical validation of gait and activity endpoints are generally performed during a controlled in-lab environment, where traditional and reference methods can be measured simultaneously with the test methods for comparison, the validation of these endpoints during longer, naturalistic, and free living conditions has been more challenging 8, 24 . Nonetheless, multiple DHT validation studies have shown evidence of measuring reliable gait and activity related metrics in both pediatric and adult populations. ...
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Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we aimed to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants’ self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home.
... Similarly to the GNSS, the algorithms of the AI/ML have also been used in the inertial area, namely in design of the inertial sensors and processing of inertial measurements [2], [57]. Whereas the former group aims at algorithms for, e.g., sensor life prediction, the latter, the more popular, group includes algorithms for inertial sensor calibration, error (or bias) estimation and compensation, and inertial data processing to get navigation information. ...
Article
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This article presents a survey of the use of AI/ML techniques in navigation and tracking applications, with a focus on the dynamical models typically involved in corresponding state estimation problems. When physics-based models are either not available or not able to capture the complexity of the actual dynamics, recent works explored the use of deep learning models. This article tradeoffs both models and presents promising solutions in between, whereby physics-based models are augmented by data-driven components. The article uses two target tracking examples, both with syntethic and real data, to illustrate the various choices of the models and their parameters, highlighting their benefits and challenges. Finally, the paper provides some conclusions and an outlook for future research in this relevant area.
... Disabilities in the UE leads to arm and hand impairments, compromising quality of life, daily independence and professional and social inclusion [1]. After stroke, neuroplascity phenomenon, elicited by repetitive and intense therapy, improves the rehabilitation progress, where robotic rehabilitation have demonstrated advantages over conventional rehabilitation techniques [2]. of sensors and the emergence of more sophisticated machine learning algorithms [11]. Many studies in the literature claim to use these systems to provide information about the quality of a patient's movement. ...
Article
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Health personnel are often unavailable for supervised robot-aided neurorehabilitation in hospitals, and patients are usually challenged by transportation issues to get to hospital. Thus, a discontinuity between therapy in hospital and at home appears slowing down the upper extremity mobility recovery. The aim of this work was to develop a system, based on wearable devices and EMG armband, able to assess the quality of the upper extremity joint movements and intelligently guide the patients during a home-based rehabilitation. This system fuses a classification model together with a dynamic time warping analysis. The classification model was trained with upper extremity joint movements gathered from clinicians, obtaining more than 80% of accuracy using only five joint angles. Then, the system was tested in two post-stroke patients and a healthy subject. The results suggest that the proposed system can be: (i) a useful tool for clinicians to evaluate the rehabilitation therapy; and (ii) an intelligent system able to make decision based on the quality of the activity executed at home.
... When the analyzed pathology or condition involves the presence of motor symptoms, among the main candidate wearable sensor technologies for human movement analysis, inertial measurement units (IMUs) stand out as an effective solution that has gained a leading role during the last two decades. They are very powerful, small, lightweight, and relatively inexpensive, and can be easily worn on proximal as well as on distal body parts, such as the forearm, hand, and fingers, and are thus able to easily move the analysis out of the lab, when compared to the stereophotogrammetric counterpart [6]. Owing to recent technological advancements, IMUs have become suitable for integration into wearable devices in clinical settings, also allowing for the capturing of motion-related data in natural unconstrained environments, especially when they are integrated into commercial devices, such as smartphones and smartwatches. ...
... In addition, often limited actionable insights are garnered from these metrics to appreciably change the course of a rehabilitation plan. New motion sensor technology now allows spontaneous monitoring of patients in more ecologically valid environments like home or the community (8)(9)(10)(11)(12). The inertial measurement unit (IMU) of smartphones can collect kinematic and spatiotemporal parameters for real-time analysis without the need for costly and inconvenient wearable devices (13)(14)(15)(16)(17). ...
Article
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Objective Self-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored. Methods Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High). Results The main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals' self-evaluated function as defined by LEFS categorization ( n = 132) and many variables differ between groups. Velocity was found to have the strongest effect size. Discussion When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.
... To assess walking ability, many different scales, tests and clinical instruments have been proposed. The gold standard for the investigation of gait impairment is the stereophotogrammetric gait analysis combined with electromyography; the use of force platforms and wearable inertial devices have also recently become frequently used [15][16][17]. Despite the possible disadvantages of using clinical scales or timed tests, they are still measures of first choice among healthcare professionals. ...
Article
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Determining the walking ability of post-stroke patients is crucial for the design of rehabilitation programs and the correct functional information to give to patients and their caregivers at their return home after a neurorehabilitation program. We aimed to assess the convergent validity of three different walking tests: the Functional Ambulation Category (FAC) test, the 10-m walking test (10MeWT) and the 6-minute walking test (6MWT). Eighty walking participants with stroke (34 F, age 64.54 ± 13.02 years) were classified according to the FAC score. Gait speed evaluation was performed with 10MeWT and 6MWT. The cutoff values for FAC and walking tests were calculated using a receiver-operating characteristic (ROC) curve. Area under the curve (AUC) and Youden's index were used to find the cutoff value. Statistical differences were found in all FAC subgroups with respect to walking speed on short and long distances, and in the Rivermead Mobility Index and Barthel Index. Mid-level precision (AUC > 0.7; p < 0.05) was detected in the walking speed with respect to FAC score (III vs. IV and IV vs. V). The confusion matrix and the accuracy analysis showed that the most sensitive test was the 10MeWT, with cutoff values of 0.59 m/s and 1.02 m/s. Walking speed cutoffs of 0.59 and 1.02 m/s were assessed with the 10MeWT and can be used in FAC classification in patients with subacute stroke between the subgroups able to walk with supervision and independently on uniform and non-uniform surfaces. Moreover, the overlapping walking speed registered with the two tests, the 10MeWT showed a better accuracy to drive FAC classification.
... In this context, the use of wearable accelerometers appears particularly intriguing. Indeed, previous studies aimed to assess workers' exposure to biomechanical risk factors in occupational contexts highlighted their ability to collect data continuously over long periods of time and their unobtrusiveness for the tested subject (Roman-Liu et al., 1996;Estill et al., 2000;Hansson et al., 2001;Søgaard et al., 2001;Amasay et al., 2010;Korshøj et al., 2014;Schall et al., 2016;West et al., 2018;Lim and D'Souza, 2020;Picerno et al., 2021). Based on the aforementioned considerations, the present study aims to characterize the main features associated with UL use in HCWs during the execution of tasks commonly performed within a regular shift using a simple setup based on two wrist-worn accelerometers. ...
Chapter
Due to the continuous and prolonged exposure to highly physically demanding tasks, healthcare workers (HCWs) are at risk to develop low back and upper limb (UL) musculoskeletal disorders (MSD). Since repetitiveness and movement asymmetries have been hypothesized to play an important role on the development of UL-MSD, in this study we propose an approach based on the use of wearable accelerometers to quantitatively characterize the main features of UL use during actual working tasks. To this aim, we tested thirty full-time professional HCWs which operate in wards characterized by different profiles of risk assessed using the “Movement and Assistance of Hospital Patients” (MAPO) technique. During a regular shift day, their activity was simultaneously monitored both using wrist-worn accelerometers and direct visual observation. Accelerations were processed to calculate several metrics associated with intensity and symmetry of use of UL. The results showed that among the daily routine activities, patient hygiene requires the most intense use of the UL, while meal distribution is the most asymmetrical. The knowledge of intensity and asymmetry of UL use associated to specific working tasks might represent a useful tool to highlight potentially harmful condition and plan suitable ergonomic interventions.KeywordsWearableAccelerometerUpper LimbHealth care workers
... This approach can help monitors take the necessary action promptly to prevent and mitigate dangerous conditions. Currently, HAR is widely used in healthcare, behavioural judgment, gait analysis and motor status recognition [3]. ...
Article
Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.
... Optimal ways that show potential for improving patient-centeredness based on feedback from these surveys include using video over audio-only options, attempting to reserve remote care interactions for follow-up visits only, if possible, after having the initial evaluation in person, and improving the ability to make additional assessments remotely (e.g., gait and movement analysis, objective functional performance). Emerging technologies with sensors, biofeedback, and the ability for remote monitoring and asynchronous interactions with the clinician [36,37], have the potential to improve the quality of information available to clinicians in this setting. This in turn can likely increase certainty and improve assurance provided to patients, which can lead to greater therapeutic alliance. ...
Article
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Background Physical therapy services delivered remotely are becoming more common. The purpose was to summarize the acceptability and patient-centeredness of remotely delivered physical therapy. Methods This was a survey study. Patients and clinicians from physical therapy clinics in the US Military Health System were asked to provide feedback at the conclusion of each remote visit. Platform, reason for care, components of physical therapy delivered and received, satisfaction, and perception of patient-centeredness were collected. Results were summarized as proportions and frequencies. Results Feedback was provided by physical therapists for 250 visits and from patients for 61 visits. Most visits were completed using audio only ( n = 172; 68.8%) while the rest integrated video capability ( n = 78; 31.2%). Overall patients perceived their care was patient-centered either completely or very much. Over 90% of visits were perceived by physical therapists as being highly patient centered. For 53.2% of visits, patients thought that same visit would have been even more impactful in person and for 52.4% of visits, physical therapists thought the visit would have been more impactful in person. Conclusion Even though remotely provided physical therapy care was rated by patients to be patient-centered, approximately half of the patients responding felt the same physical therapy visit would have been more impactful in person. Similarly, physical therapists felt that their intervention would have been more impactful in person for approximately half of all visits. Physical therapy care delivered remotely was patient-centered and an acceptable alternative to in-person care for both patients and physical therapists.
... Similarly to the GNSS, the algorithms of the AI/ML have also been used in the inertial area, namely in design of the inertial sensors and processing of inertial measurements [2], [59]. Whereas the former group aims at algorithms for, e.g., sensor life prediction, the latter, the more popular, group includes algorithms for inertial sensor calibration, error (or bias) estimation and compensation, and inertial data processing to get navigation information. ...
Book
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This book provides a comprehensive overview of the use of vision attentive technology and artificial intelligence methodologies for functional mobility assessment in elderly populations. Vision Attentive Technology and Functional Mobility Assessment in Elderly Healthcare, begins with a general introduction to vision-attentive technology and its uses in the care of older people. Next it examines functional mobility in senior populations and offers a critique of the methods used today for evaluation. The authors then present several artificial intelligence approaches and vision-aware systems used for screening age related diseases such as Parkinson's disease and sarcopenia. The book also presents the difficulties and possibilities of using visual attentive technology to identify functional impairments caused by aging. This book would be helpful to researchers in the field of healthcare, especially those interested in using technology to enhance patient outcomes. Geriatricians, physical therapists, and occupational therapists who treat older patients will also benefit from reading this book. It will also be helpful to readers who are studying biomedical engineering, artificial intelligence, and healthcare.
Article
Purpose The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences. Design/methodology/approach The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction. Findings The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction. Originality/value This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.
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The analysis of the Hip and Knee (HK) joint angles during single-leg stance (SLS) activity contributes to a great understanding of the bio-mechanical mechanisms and balance maintenance across different age groups. Comprehending how these joints operate in the dynamic state is critical for identifying age-related changes in joint control and stability which can contribute to reducing the risk of falls and improving mobility among people. The possibility of revealing the health of HK joints without resorting to an MRI scan is the significance of this work. By obtaining the data on joint angles during the SLS test, we succeeded in identifying those with a higher risk of HK issues and thus the possibility of early intervention and treatment. Our proposed work is going to use a pose estimation technique that will track the trajectories of HK angles followed by association with instabilities or compromised balance in this way, we can detect the affected joint and evaluate the severity of the issue. The study found stable mean and standard deviation values for HK joints in a young participant, both hips (107.14 ± 5, 96.42 ± 7) and both knees (36.76 ± 7, 44.30 ± 4), these values align with the expected norm (110–120° for the hip and 45–65° for the knee) which indicates stable results. while elderly participants showed high variability and low mean values for both hips (65.42 ± 77, 85 ± 76.67) and both knees (4.15 ± 10.8, 7 ± 18) indicating concerns about joint health and stability. The evaluation of HK joint angles through SLS activity offers new insights.
Article
Understanding the complex three-dimensional (3D) dynamic interactions between self-contained breathing apparatus (SCBA) and the human torso is critical to assessing potential impacts on firefighter health and informing equipment design. This study employed a multi-inertial sensor fusion technology to quantify these interactions. Six volunteer firefighters performed walking and running experiments on a treadmill while wearing the SCBA. Calculations of interaction forces and moments from the multi-inertial sensor technology were validated against a 3D motion capture system. The predicted interaction forces and moments showed good agreement with the measured data, especially for the forces (normal and lateral) and moments (x- and z-direction components) with relative root mean square errors (RMSEs) below 9.4%, 7.7%, 7.7%, and 7.8%, respectively. Peak pack force reached up to 150 N, significantly exceeding the SCBA's intrinsic weight during SCBA carriage. The proposed multi-inertial sensor fusion technique can effectively evaluate the 3D dynamic interactions and provide a scientific basis for health monitoring and ergonomic optimization of SCBA systems for firefighters.
Chapter
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The development of sensors that can be discreetly worn on the body or become part of clothing items has opened up numerous possibilities for monitoring patients in the field over long periods of time. Wearable technology addresses an important issue in the treatment of patients undergoing rehabilitation. Wearable technology allows clinicians to collect data from anywhere so they can respond to these issues. Through wearable systems, direct observations can be made regarding the impact of clinical interventions on mobility, independence level and quality of life. This section explains the place and advantages of wearable technologies in different rehabilitation fields. The usage areas of wearable technology are rapidly developing and many clinical studies are being conducted. The potential impact of these technologies on the clinical practice of rehabilitation is increasing day by day. Although the main focus of clinical evaluation techniques is on methods applied in the clinical setting, wearable technology has the potential to direct this focus beyond field clinical settings.
Article
Background: Monitoring spine kinematics is crucial for applications like disease evaluation and ergonomics analysis. However, the small scale of vertebrae and the number of degrees of freedom present significant challenges for noninvasive and convenient spine kinematics estimation. Methods: This study developed a dynamic optimization framework for wearable spine motion tracking at the intervertebral joint level by integrating smartphone videos and Inertia Measurement Units (IMUs) with dynamic constraints from a thoracolumbar spine model. Validation involved motion data from 10 healthy males performing static standing, dynamic upright trunk rotations, and gait. This data included rotations of ten IMUs on vertebrae and virtual landmarks from three smartphone videos preprocessed by OpenCap, an application leveraging computer vision for pose estimation. The kinematic measures derived from the optimized solution were compared against simultaneously collected infrared optical marker-based measurements and in vivo literature data. Solutions only based on IMUs or videos were also compared for accuracy evaluation. Results: The proposed optimization approach closely matched the reference data in the intervertebral or segmental rotation range, demonstrating minimal angular differences across all motions and the highest correlation in 3D rotations (maximal Pearson and intraclass correlation coefficients of 0.92 and 0.94, respectively). Time-series changes of joint angles also aligned well with the optical-marker reference. Conclusion: Dynamic optimization of the spine simulation that integrates IMUs and computer vision outperforms the single-modality method. Significance: This markerless 3D spine motion capture method holds potential for spinal health assessment in large cohorts in real-world settings without dedicated laboratories.
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In biomechanics, movement is typically recorded by tracking the trajectories of anatomical landmarks previously marked using passive instrumentation, which entails several inconveniences. To overcome these disadvantages, researchers are exploring different markerless methods, such as pose estimation networks, to capture movement with equivalent accuracy to marker-based photogrammetry. However, pose estimation models usually only provide joint centers, which are incomplete data for calculating joint angles in all anatomical axes. Recently, marker augmentation models based on deep learning have emerged. These models transform pose estimation data into complete anatomical data. Building on this concept, this study presents three marker augmentation models of varying complexity that were compared to a photogrammetry system. The errors in anatomical landmark positions and the derived joint angles were calculated, and a statistical analysis of the errors was performed to identify the factors that most influence their magnitude. The proposed Transformer model improved upon the errors reported in the literature, yielding position errors of less than 1.5 cm for anatomical landmarks and 4.4 degrees for all seven movements evaluated. Anthropometric data did not influence the errors, while anatomical landmarks and movement influenced position errors, and model, rotation axis, and movement influenced joint angle errors.
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Mobility deficits are not uncommon in persons with neuromuscular and musculoskeletal disorders. This can have a negative impact in terms of morbidity, mortality, quality of life and activities of daily living (1). Conventionally, mobility and physical activity have been measured in a clinical and laboratory setting by a qualified health professional and is thus called supervised assessment (1). It is either a qualitative or quantitative one- time snapshot evaluation of physical activity, mobility or motion and is highly influenced by factors such as the Hawthorne effect, the time of day when the measurement was taken, white coat and reverse white coat effect (1). Moreover, supervised mobility assessment may have many limitations such as limited ecological validity, lack of patient-centered focus, inability to record real-world challenges, absence of real-time feedback, lack of ability to consider patient's environment, and an omission in observing the range of performance across the day or week (1). To tackle the above mentioned limitations, recently unsupervised mobility assessment of mobility and physical activity using mobile health technology has emerged as an alternative to conventional supervised assessment (1-3). Significant differences may be observed when comparing identical mobility outcomes measured under supervised and unsupervised conditions (1, 3). A systematic review revealed significant variations of 40-180% in identical mobility measures acquired from the same participants across different settings (1). The disparities between supervised and unsupervised measurements are notably greater than the effects observed in treatment interventions. Minor to moderate treatment effects may be overshadowed by these substantial differences in measurement modes (1). Unsupervised assessment holds the potential to address the limitations of supervised assessment as it is patient-centered, ecologically valid, capable of recording fluctuating and rare events, unaffected by the white coat and Hawthorne effects, provides real-time treatment feedback, records real-world challenges, is influenced by a person's mood and fatigue, considers the environment, and reports performance across the day or week (1). Moreover, unsupervised assessment does not require the presence of a trained professional, or the patient to report to a clinic or hospital, and thus can be of great value in rural environments and in tele-medicine/rehabilitation. Not only would this be cost effective, it will also decrease the load on the health care system and the need for health care human resource. ---Continue
Article
Introduction: Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients. Methods: For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed. Results: All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation. Conclusions: Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure. Prospero registration: CRD42022355460.
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Pediatric critical care medicine (PCCM) fellows must develop competence in central venous catheter (CVC) placement. The impact of experiential learning opportunities in the clinical context on PCCM fellow CVC placement skill acquisition remains unknown. We sought to measure femoral CVC placement skill acquisition during fellowship and compare fellow to attending skill. We performed a prospective observational cohort study of PCCM fellows at the University of Colorado from 2019 to 2021. Femoral CVC placement skill was measured by attending evaluation of level of the supervision (LOS) required for the fellow, and hand motion analysis (HMA) on simulation task trainer. Competence in femoral CVC placement was defined as LOS ≥ 4 (can perform this skill independently) on a 5-point Likert scale. We compared fellow skill in femoral CVC placement to years of training and number of femoral CVCs placed. We also compared third-year fellow and attending HMA measurements. We recruited 13 fellows and 6 attendings. Fellows placed a median of 8 (interquartile range 7, 11) femoral CVCs during the study period. All fellows who reached third-year of fellowship during the study period achieved competence. Longitudinal analysis demonstrated improvement in CVC placement skill by both LOS and HMA as years of fellowship and number of femoral CVCs placed increased. Few third-year fellows achieved attending level skill in femoral CVC placement as measured by HMA. PCCM fellows acquired skill in CVC placement during fellowship and achieved competence in the procedure, but most did not reach attending level of skill.
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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.
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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.
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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. What this paper adds Both virtual reality rehabilitation and conventional occupational therapy were effective for upper‐limb training. Virtual reality training was superior in improving dexterity, performance of activities of daily living, and active forearm supination motion. The effect of virtual reality training was significant in children with more severe motor impairments.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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