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Sensor Integration for Gait Analysis
Abstract
Advancements in Microelectromechanical systems (MEMS) have enabled the manufacture of affordable and efficient wearable devices. In sensor-based gait analysis, motion and biofeedback sensor devices are easily attached to different parts of the body. Instrumentation of gait using different sensor technologies enables researchers and clinicians to capture high-resolution quantitative motion data within and beyond the lab. Integration of advanced sensor technologies provides objective and rater-independent multimodal outcomes that complement established clinical examination. Multi-modal data capture in ecologically valid, patient-relevant habitual settings opens new possibilities to monitor fluctuating and rare incidents by informing different aspects of impaired gait. Interconnected device communication and the Internet of Things (IoT) provide the infrastructural platform to enable remote gait assessment. However, an extended period of motion data recorded by different sensor technologies results in a vast amount of unlabelled data. Computational methods and artificial intelligence techniques (e.g., data mining) provide opportunities to manage data collected in unsupervised environments. Although technological advancement and algorithms promote remote gait assessment, more work needs to be done in terms of analytical and clinical validation to achieve robust and reliable gait analysis tools that contribute to better rehabilitation and treatment.
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Introduction
Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users’ perspective on the device.
Methods and analysis
This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.
After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users’ perspective on the deployed technology and relevance of the mobility assessment.
Ethics and dissemination
The study has been granted ethics approval by the centre’s committees (London—Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available.
Trial registration number
ISRCTN (12246987).
People with dementia are at risk of mobility decline. In this study, we measured changes in quantitative gait measures over a maximum 10-week period during the course of a psychogeriatric admission in older adults with dementia, with the aims to describe mobility changes over the duration of the admission, and to determine which factors were associated with this change. Fifty-four individuals admitted to a specialized dementia inpatient unit participated in this study. A vision-based markerless motion capture system was used to record participants’ natural gait. Mixed effect models were developed with gait measures as the dependent variables and clinical and demographic variables as predictors. We found that gait stability, step time, and step length decreased, and step time variability and step length variability increased over 10 weeks. Gait stability of men decreased more than that of women, associated with an increased sacrum mediolateral range of motion over time. In addition, the sacrum mediolateral range of motion decreased in those with mild neuropsychiatric symptoms over 10 weeks, but increased in those with more severe neuropsychiatric symptoms. Our study provides evidence of worsening of gait mechanics and control over the course of a hospitalization in older adults with dementia. Quantitative gait monitoring in hospital environments may provide opportunities to intervene to prevent adverse events, decelerate mobility decline, and monitor rehabilitation outcomes.
Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, impact of age (e.g., older adults, OA), pathology (e.g., Parkinson's Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. Level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC2,1= 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC2,1 = 0.998, 0.940, 0.856, 0.998) found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition.
Gait analysis is important in the field of healthcare, due to its close relationship to chronic diseases. With the development of the Internet of Health Things (IoHT), long-term gait monitoring and corresponding analysis can be performed remotely, reducing a patient’s time and traffic cost, while providing doctors more valuable gait information. In this paper, we present a piezoelectric insole gait monitoring system and its use in an IoHT architecture. Through the experimental results, the high detection sensitivity of 3.56 kPa and responsivity of 2.35 mV/kPa are achieved. Besides, computer software is developed with Qt platform, and the cloud database is also established. The plantar pressure distribution and center of pressure (CoP) trajectory are provided for further analysis, thereby satisfying the need for analyzing various diseases. Furthermore, the assembled system can continuously work for 16 hours, indicating its successful utilization when long-term gait monitoring is required. The presented work provides a feasible means for real-time, long-term, and accurate gait monitoring, prompting the development of gait analysis in the IoHT.
The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.
Introduction:
Gait impairment is common in neurodegenerative disorders. Specifically, gait variability-the stride-to-stride fluctuations in distance and time-has been associated with neurodegeneration and cognitive impairment. However, quantitative comparisons of gait impairments across the cognitive spectrum of dementias have not been systematically investigated.
Methods:
Older adults (N = 500) with subjective cognitive impairment, Parkinson disease (PD), mild cognitive impairment (MCI), PD-MCI, Alzheimer's disease (AD), PD-dementia, Lewy body dementia, and frontotemporal dementia, as well cognitive normal controls, who were assessed for their gait and cognitive performance.
Results:
Factor analyses grouped 11 quantitative gait parameters and identified four independent gait domains: rhythm, pace, variability, and postural control, for group comparisons and classification analysis. Among these domains, only high gait variability was associated with lower cognitive performance and accurately discriminated AD from other neurodegenerative and cognitive conditions.
Discussion:
Our findings indicate that high gait variability is a marker of cognitive-cortical dysfunction, which can help to identify Alzheimer's disease dementia.
Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.
The trends of wearable health monitoring systems have led to growing demands for gait-capturing devices. However, comfortability and durability under repeated stress are still challenging to achieve in existing sensor-enabled footwear. Herein, a flexible textile piezoresistive sensor (TPRS) consisting of a reduced graphene oxide (rGO)-cotton) fabric electrode and an Ag fabric circuit electrode is proposed. Based on the mechanical and electrical properties of the two fabric electrodes, the TPRS exhibits superior sensing performance, with a high sensitivity of 3.96 kPa-1 in the lower pressure range of 0–36 kPa, wide force range (0–100 kPa), fast response time (170 ms), remarkable durability stability (1000 cycles) and detection ability in different pressures ranges. For the prac-tical application of capturing plantar pressure, six TPRSs were mounted on a flexible printed circuit board and integrated into an insole. The dynamic plantar pressure distribution during walking was derived in the form of pressure maps. The proposed fully-textile piezoresistive sensor is a strong candidate for next-generation plantar pressure wearable monitoring devices.
Digital health technology (DHT), including wearable and environmental sensors, video cameras and other electronic tools, has provided new opportunities for the measurement of movement and functionality in Parkinson’s disease. Compared to current standards for evaluation of the disease (MDS-UPDRS), DHT may offer new possibilities for more frequent objective measurements of the duration, severity and frequency of disease manifestations over time, that may provide more information than periodic clinic visits. However, DHT measurement are only scientifically and medically useful if they are accurate, reliable and clinically meaningful. Verification and validation, also known as analytical validation and clinical validation, of DHT performance is important to ensure the accuracy and precision of measurements, and the specificity of findings. Given the wide range of clinical manifestations associated with Parkinson’s disease and the many tools and metrics to assess them, the challenge is to identify those that may represent a standard for use in clinical trials, and to confirm when digital measurements succeed or fall short of capturing meaningful benefits during drug development.
Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.
Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.
Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.
Gait assessment is an essential tool for clinical applications not only to diagnose
different neurological conditions but also to monitor disease progression as it
contributes to the understanding of underlying deficits. There are established methods
and models for data collection and interpretation of gait assessment within different
pathologies. This narrative review aims to depict the evolution of gait assessment from
observation and rating scales to wearable sensors and laboratory technologies, and
provide possible future directions. In this context, we first present an extensive review
of current clinical outcomes and gait models. Then, we demonstrate commercially
available wearable technologies with their technical capabilities along with their use in
gait assessment studies for various neurological conditions. In the next sections, a
descriptive knowledge for existing inertial based algorithms and a sign based guide
that shows the outcomes of previous neurological gait assessment studies are
presented. Finally, we state a discussion for the use of wearables in gait assessment
and speculate the possible research directions by revealing the limitations and
knowledge gaps in the literature.
Forced expiratory volume in one second (FEV1) is a critical parameter for the assessment of lung function for both clinical care and research in patients with asthma. While asthma is defined by variable airflow obstruction, FEV1 is typically assessed during clinic visits. Mobile spirometry (mSpirometry) allows more frequent measurements of FEV1, resulting in a more continuous assessment of lung function over time and its variability. Twelve patients with moderate asthma were recruited in a single‐center study and were instructed to perform pulmonary function tests at home twice daily for 28 days and weekly in clinic. Daily and mean subject compliance was summarized. The agreement between clinic and mobile FEV1 was assessed using correlation and Bland‐Altman analyses. The test‐retest for clinic and mobile spirometry was assessed by inter‐class correlation coefficient (ICC). Simulation was conducted to explore if mSpirometry could improve statistical power over clinic counterparts. The mean subject compliance with mSpirometry was 70% for twice‐daily and 85% for at least once‐daily. mSpirometry FEV1 were highly correlated and agreed with clinic ones from the same morning (r=0.993) and the same afternoon (r=0.988) with smaller mean difference for the afternoon (0.0019L) than morning (0.0126L) measurements. The test‐retest reliability of mobile (ICC=0.932) and clinic (ICC=0.942) spirometry were comparable. Our simulation analysis indicated greater power using dense mSpirometry than sparse clinic measurements. Overall, we have demonstrated good compliance for repeated at‐home mSpirometry, high agreement and comparable test‐retest reliability with clinic counterparts, greater statistical power, suggesting a potential for use in asthma clinical research.
Background: Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient’s habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.
Methods: A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strat- egy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extrac- tion for the narrative review.
Results: Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm’s accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.
Conclusion: This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
Measurement of the joint angle during walking in real-world environments facilitates comprehension of the adaptation strategy corresponding to road surfaces. This study investigated the differences between the joint angles in the lower limb when walking on flat road surfaces in indoor and outdoor environments. Ten healthy young males who walked on a carpet-lined corridor in the indoor environment and on an interlocking block pavement surface in the outdoor environment participated in the study. The joint angles of their lower limbs were measured using seven inertial measurement units, and the average and coefficient of variation (%CV) of the joint angular excursion in the two environments were evaluated. The %CVs of the ankle plantar flexion excursion in the early stance was 45% higher in the outdoor environment compared with that in the indoor, although the spatiotemporal parameters and joint angular excursion of the proximal joints showed no difference between the environments. Though the road surfaces were flat from a macroscopic point of view, the interlocking block pavement had stiffer and more irregular characteristics. The variability in the ankle plantar flexion motion in the early stance may be most likely affected by these surface characteristics in the real-world outdoor environment.
Wearable inertial sensors can be used to monitor mobility in real-world settings over extended periods. Although these technologies are widely used in human movement research, they have not yet been qualified by drug regulatory agencies for their use in regulatory drug trials. This is because the first generation of these sensors was unreliable when used on slow-walking subjects. However, intense research in this area is now offering a new generation of algorithms to quantify Digital Mobility Outcomes so accurate they may be considered as biomarkers in regulatory drug trials. This perspective paper summarises the work in the Mobilise-D consortium around the regulatory qualification of the use of wearable sensors to quantify real-world mobility performance in patients affected by Parkinson’s Disease. The paper describes the qualification strategy and both the technical and clinical validation plans, which have recently received highly supportive qualification advice from the European Medicines Agency. The scope is to provide detailed guidance for the preparation of similar qualification submissions to broaden the use of real-world mobility assessment in regulatory drug trials.
Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements.
Research has shown that cognitive and physical functioning of older adults can be reflected in indicators such as walking speed. While changes in cognition, mobility, or health cause changes in gait speed, often gradual variations in walking speed go undetected until severe problems arise. Discrete clinical assessments during clinical consultations often fail to detect changes in day-to-day walking speeds and do not reflect walking speeds in everyday environments, where most of the mobility issues happen. In this paper, we compare four walking speed measurement technologies to a GAITRite mat (gold standard): (1) an ultra wideband radar (covering the band from 3.3 GHz to 10 GHz), (2) a narrow band 24-GHz radar (with a bandwidth of 250 MHz), (3) a perception Neuron Motion Tracking suit, and (4) a thermal camera. Data were collected in parallel using all sensors at the same time for 10 healthy adults for normal and slow walking paces. A comparison of the sensors indicates better performance at lower gait speeds, with offsets (when compared to GAITRite) between 0.1 and 20% for the ultra wideband radar, 1.9 and 17% for the narrowband radar, 0.1 and 38% for the thermal camera, and 1.7 and 38% for the suit. This paper supports the potential of unobtrusive radar-based sensors and thermal camera technologies for ambient autonomous gait speed monitoring for contextual, privacy-preserving monitoring of participants in the community.
Background:
Previous studies found levodopa could improve the activity of the ankle joints of patients with Parkinson's disease (PD). But ankle joint movement is composed of four motion ranges. The specific changes of four motion ranges in PD remain unknown.
Objective:
The purpose of this study was to decompose the complex ankle joint movement, measure ankle joint changes before and after the acute levodopa challenge test (ALCT), and investigate the effects of these parameters on gait performance.
Methods:
29 PD patients and 30 healthy control subjects (HC) completed the Instrumented Stand and Walk (ISAW) test and gait parameters were collected by the JiBuEn gait analysis system. The percentage of improvement of gait data and the UPDRS III in the on-drug condition (ON) were determined with respect to the off-drug condition (OFF).
Results:
We observed a reduction in the heel strike angle (HS), 3-plantarflexion (3-PF) angle, and 4-dorsiflexion (4-DF) angle of ankle joints. We did not find significant difference in the toe-off angle (TO), 1-plantarflexion (1-PF) angle, and 2-dorsiflexion (2-DF) angle among three groups. Stride length improvement rate was significantly correlated with HS (rs = 0.616, P < 0.001) and 3-PF (rs = 0.639, P < 0.001) improvement rates. The improvement in the sum of rigidity items (UPDRS motor subsection item 22) was also correlated with HS (rs = 0.389, P=0.037) and 3-PF (rs = 0.373, P=0.046) improvement rates.
Conclusions:
Exogenous levodopa supplementation can significantly reduce the rigidity of patients with PD, improve their 3-PF and 4-DF of ankle joint kinematic parameters, and ultimately enhance their gait.
Digital medicine is an interdisciplinary field, drawing together stakeholders with expertise in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. While this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes 1) verification, 2) analytical validation, and 3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.
Electromyographic (EMG) raw signals are sensitive to intrinsic and extrinsic factors. Consequently, EMG normalization is required to draw proper interpretations of standardized data. Specific recommendations are needed regarding a relevant EMG normalization method for participants who show atypical EMG patterns, such as post-stroke subjects. This study compared three EMG normalization methods ("isometric MVC", "isokinetic MVC", "isokinetic MVC kinematic-related") on muscle activations and the antagonist-agonist co-contraction index. Fifteen post-stroke subjects and fifteen healthy controls performed active elbow extensions, followed by isometric and isokinetic maximum voluntary contractions (MVC). Muscle activations were obtained by normalizing EMG envelopes during active movement using a reference value determined for each EMG normalization method. The results showed no significant difference between the three EMG normalization methods in post-stroke subjects on muscle activation and the antagonist-agonist co-contraction index. We highlighted that the antagonist-agonist co-contraction index could underestimate the antagonist co-contraction in the presence of atypical EMG patterns. Based on its practicality and feasibility, we recommend the use of isometric MVC as a relevant procedure for EMG normalization in post-stroke subjects. We suggest combined analysis of the antagonist-agonist co-contraction index and agonist and antagonist activations to properly investigate antagonist co-contraction in the presence of atypical EMG patterns during movement.
Background
Slowness of walking is one of the very first signs of aging and is considered a marker for overall health that is strongly associated with mortality risk. In this study we sought to disentangle the clinical drivers of the association between gait and mortality.
Methods
We included 4,490 participants of the Rotterdam Study who underwent a gait assessment between 2009 and 2015 and were followed-up for mortality until 2018. Gait was assessed with an electronic walkway and summarized into the domains Rhythm, Phases, Variability, Pace, Tandem, Turning, and Base of Support. Cox models adjusted for age, sex, and height were built and consecutively adjusted for six categories of health indicators (lifestyle, musculoskeletal, cardiovascular, pulmonary, metabolic, and neurological). Analyses were repeated in comorbidity-free individuals.
Results
Multiple gait domains were associated with an increased risk of mortality, including Pace (HR per SD worse gait, adjusted for other domains: 1.34 [1.19-1.50]), Rhythm (HR: 1.12 [1.02-1.23]) and Phases (HR: 1.12 [1.03-1.21]). Similarly, a 0.1 m/s decrease in gait speed was associated with a 1.21 [1.15-1.27] times higher hazard of mortality (HR fully adjusted: 1.14 [1.08-1.20]). In a comorbidity-free subsample, the hazard ratio per 0.1 m/s decrease in gait speed was 1.25 [1.09-1.44]. Cause-specific mortality analyses revealed an association between gait speed and multiple causes of death.
Conclusions
Several gait domains were associated with mortality risk, including Pace which primarily represents gait speed. The association between gait speed and mortality persisted after an extensive adjustment for covariates, suggesting that gait is a marker for overall health.
Background Falls in elderly people are a major health burden, especially in the long-term care environment. Yet little objective evidence is available for how and why falls occur in this population. We aimed to provide such evidence by analysing real-life falls in long-term care captured on video.
Methods We did this observational study between April 20, 2007, and June 23, 2010, in two long-term care facilities in British Columbia, Canada. Digital video cameras were installed in common areas (dining rooms, lounges, hallways). When a fall occurred, facility staff completed an incident report and contacted our teams so that we could collect video footage. A team reviewed each fall video with a validated questionnaire that probed the cause of imbalance and activity at the time of falling. We then tested whether differences existed in the proportion of participants falling due to the various causes, and while engaging in various activities, with generalised linear models, repeated measures logistic regression, and log-linear Poisson regression.
Findings We captured 227 falls from 130 individuals (mean age 78 years, SD 10). The most frequent cause of falling was incorrect weight shifting, which accounted for 41% (93 of 227) of falls, followed by trip or stumble (48, 21%), hit or bump (25, 11%), loss of support (25, 11%), and collapse (24, 11%). Slipping accounted for only 3% (six) of falls. The three activities associated with the highest proportion of falls were forward walking (54 of 227 falls, 24%), standing quietly (29 falls, 13%), and sitting down (28 falls, 12%). Compared with previous reports from the long-term care setting, we identified a higher occurrence of falls during standing and transferring, a lower occurrence during walking, and a larger proportion due to centre-of-mass perturbations than base-of-support perturbations.
Interpretation By providing insight into the sequences of events that most commonly lead to falls, our results should lead to more valid and effective approaches for balance assessment and fall prevention in long-term care.
Importance
Gait speed is a well-known indicator of risk of functional decline and mortality in older adults, but little is known about the factors associated with gait speed earlier in life.
Objectives
To test the hypothesis that slow gait speed reflects accelerated biological aging at midlife, as well as poor neurocognitive functioning in childhood and cognitive decline from childhood to midlife.
Design, Setting, and Participants
This cohort study uses data from the Dunedin Multidisciplinary Health and Development Study, a population-based study of a representative 1972 to 1973 birth cohort in New Zealand that observed participants to age 45 years (until April 2019). Data analysis was performed from April to June 2019.
Exposures
Childhood neurocognitive functions and accelerated aging, brain structure, and concurrent physical and cognitive functions in adulthood.
Main Outcomes and Measures
Gait speed at age 45 years, measured under 3 walking conditions: usual, dual task, and maximum gait speeds.
Results
Of the 1037 original participants (91% of eligible births; 535 [51.6%] male), 997 were alive at age 45 years, of whom 904 (90.7%) had gait speed measured (455 [50.3%] male; 93% white). The mean (SD) gait speeds were 1.30 (0.17) m/s for usual gait, 1.16 (0.23) m/s for dual task gait, and 1.99 (0.29) m/s for maximum gait. Adults with more physical limitations (standardized regression coefficient [β], −0.27; 95% CI, −0.34 to −0.21; P < .001), poorer physical functions (ie, weak grip strength [β, 0.36; 95% CI, 0.25 to 0.46], poor balance [β, 0.28; 95% CI, 0.21 to 0.34], poor visual-motor coordination [β, 0.24; 95% CI, 0.17 to 0.30], and poor performance on the chair-stand [β, 0.34; 95% CI, 0.27 to 0.40] or 2-minute step tests [β, 0.33; 95% CI, 0.27 to 0.39]; all P < .001), accelerated biological aging across multiple organ systems (β, −0.33; 95% CI, −0.40 to −0.27; P < .001), older facial appearance (β, −0.25; 95% CI, −0.31 to −0.18; P < .001), smaller brain volume (β, 0.15; 95% CI, 0.06 to 0.23; P < .001), more cortical thinning (β, 0.09; 95% CI, 0.02 to 0.16; P = .01), smaller cortical surface area (β, 0.13; 95% CI, 0.04 to 0.21; P = .003), and more white matter hyperintensities (β, −0.09; 95% CI, −0.15 to −0.02; P = .01) had slower gait speed. Participants with lower IQ in midlife (β, 0.38; 95% CI, 0.32 to 0.44; P < .001) and participants who exhibited cognitive decline from childhood to adulthood (β, 0.10; 95% CI, 0.04 to 0.17; P < .001) had slower gait at age 45 years. Those with poor neurocognitive functioning as early as age 3 years had slower gait in midlife (β, 0.26; 95% CI, 0.20 to 0.32; P < .001).
Conclusions and Relevance
Adults’ gait speed is associated with more than geriatric functional status; it is also associated with midlife aging and lifelong brain health.
Objective:
We aimed to refine the hypothesis that dementia has a unique signature of gait impairment reflective of underlying pathology by considering two dementia subtypes, Alzheimer's disease (AD) and Lewy body disease (LBD), and exploring the role of cognition in disease-specific gait impairments.
Background:
Accurately differentiating AD and LBD is important for treatment and disease management. Early evidence suggests gait could be a marker of dementia due to associations between discrete gait characteristics and cognitive domains.
Updated hypothesis:
We hypothesize that AD and LBD have unique signatures of gait, reflecting disease-specific cognitive profiles and underlying pathologies. An exploratory study included individuals with mild cognitive impairment or dementia due to LBD (n = 45) and AD (n = 36) and 29 older adult controls. An instrumented walkway quantified 16 gait characteristics reflecting five independent domains of locomotion (pace, rhythm, variability, asymmetry, and postural control). The LBD group demonstrated greater impairments in asymmetry and variability compared with AD; both groups were more impaired in pace and variability domains than controls. Executive dysfunction explained 11% of variance for gait variability in LBD, whereas global cognitive impairment explained 13.5% of variance in AD; therefore, gait impairments may reflect disease-specific cognitive profiles. With a refined hypothesis that AD- and LBD-specific signatures of gait reflect discrete pathologies, future studies must examine the relationship between a validated model of gait with neural networks, using recognized biomarkers and postmortem follow-up.
Major challenges for hypothesis:
Differential diagnosis of AD and LBD used appropriate criteria and required consensus from an expert diagnostic panel to improve diagnostic accuracy. Future work should follow the framework set out in Parkinson's disease to establish unique signatures of gait as proxy measures of disease-specific pathology; that is, use a validated gait model to explore the progressive relationship between gait, cognition, and pathology.
Linkage to other major theories:
These exploratory findings support the theory of interacting cognitive-motor networks, as the gait-cognition relationship may reflect cognitive control over motor networks. Unique signatures of gait may reflect different temporal patterns of pathological burden in neural areas related to cognitive and motor function.
This paper firstly introduces common wearable sensors, smart wearable devices and the key application areas. Since multi-sensor is defined by the presence of more than one model or channel, e.g. visual, audio, environmental and physiological signals. Hence, the fusion methods of multi-modality and multi-location sensors are proposed. Despite it has been contributed several works reviewing the stateoftheart on information fusion or deep learning, all of them only tackled one aspect of the sensor fusion applications, which leads to a lack of comprehensive understanding about it. Therefore, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of the fusion methods of wearable sensors. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning. Finally, the open research issues that need further research and improvement are identified and discussed.
Among diverse wearable techniques, insole‐based plantar pressure monitoring systems have surged as a leading technology to monitor patient's chronic disease progression. Such technological feat has been made possible due to the strong correlation between gait and disease status. Hence, insole‐based plantar pressure monitoring techniques are growing rapidly worldwide; with several research institutions and enterprises showing an increased interest in the field. This review intends to first explain the working principles of mainstream insole plantar sensing techniques and design considerations such as sensing material selection and electronics design requirements, and then the state‐of‐the‐art algorithms for plantar pressure distribution reconstruction. Following, this article will discuss disease monitoring applications and the extraction of disease features. Finally, insight regarding common challenges and their potential solutions within the field would be elucidated. Wearable insole‐based plantar pressure monitoring systems have surged as a leading technology to monitor chronic disease progression. This article reviews the insole pressure sensing techniques, material selection, sensor constructions, electronic design considerations, and the algorithms for plantar pressure distribution reconstruction, disease monitoring, and features extraction. This article also discusses insight regarding common challenges and their potential solutions within the field.
Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.
Age-related changes may affect the performance during fast walking speed. Although, several studies have been focused on the contribution of the prefrontal cortex (PFC) during challenging walking tasks, the neural mechanism underling fast walking speed in older people remain poorly understood. Therefore, the aim of this study was to investigate the influence of aging on PFC activity during overground walking at preferred and fast speeds. Twenty-five older adults (67.37±5.31 years) and 24 young adults (22.70±1.30 years) walked overground in two conditions: preferred speed and fast walking speed. Five trials were performed for each condition. A wireless functional near-infrared spectroscopy (fNIRS) system measured PFC activity. Gait parameters were evaluated using the GAITRite system. Overall, older adults presented higher PFC activity than young adults in both conditions. Speed-related change in PFC activity was observed for older adults, but not for young adults. Older adults significantly increased activity in the left PFC from the preferred to fast walking condition whereas young adults had similar levels of PFC activity across conditions. Our findings suggest that older adults need to recruit additional prefrontal cognitive resources to control walking, indicating a compensatory mechanism. In addition, left PFC seems to be involved in the modulation of gait speed in older adults.
The rapid aging of the population worldwide requires increased attention from healthcare providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this article, we investigate a methodology for tracking the evolution of the behavior trajectories over long periods (years) using high-dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms: Principal Component Analysis and t-distributed Stochastic Neighbor Embedding. Moreover, our tracking algorithm in the original high-dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over 10 years. We used the TigerPlace electronic health records to understand the residents’ behavior patterns and to evaluate the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the electronic health records, providing strong support for a prospective deployment of the approach.
Wearable health technologies are becoming pervasive in modern society, due to cost, marketing strategies and the gamification of health. Consequently, these devices have become an interest to healthcare researchers and medical professionals. However, these devices typically come with their own proprietary software—standalone technology that makes daily use for patient management difficult. Challenges surrounding data access present steep learning curves and barriers to entry for many researchers, with computer science commonly being a prerequisite.
This chapter explores frameworks for wearable technologies and does so from two angles. The first angle explores the concept of frameworks from the position of systems and data management, that is, software frameworks. In this regard, the chapter presents the challenges and complexities researchers may face when attempting to extract data from these devices. We present several approaches to that researchers can use to collect data, which cater for different levels of technological capability. In presenting these approaches, the lack of digital frameworks, in the context of standardization and governance, is identified. This presents the second angle of frameworks explored by this chapter. There is a need for a National Digital Framework that is tailor-made for wearable technology, but the complexity and heterogeneity of current digital frameworks is indicative of how challenging this process will be. By exposing these needs and presenting researchers with a range of approaches for wearable technology data extraction, it is hoped that researchers of any specialism can drive the research and development of wearable health technologies and the frameworks that underpin them.
Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks’ capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantified human balance from a wide demographic of human participants (163 females and males aged 18-86) for varied standing conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, eyes-closed foam surface) was used. Classification was based on one of the several indicators of fall-risk tied to the fear of falling: the clinically-used Falls Efficacy Scale (FES) assessment. For human fall-risk prediction, the deep learning architecture implemented comprised of: Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), One Dimensional Convolutional Neural Network (1D-CNN), and a proposed One-One-One Deep Neural Network. Results showed that our One-One-One Deep Neural Networks algorithm outperformed the other aforementioned algorithms and state-of-the-art models on the same dataset. With an accuracy, precision, and sensitivity of 99.9%, 100%, 100%, respectively at the 12th epoch, we found that our proposed One-One-One Deep Neural Network model is the most efficient neural network in predicting human’s fall-risk (based on the FES measure) using the force-plate time series signal. This is a novel methodology for an accurate prediction of human risk of fall.
Background:
There is a lack of recommendations for selecting the most appropriate gait measures of Parkinson's disease (PD)-specific dual-task costs to use in clinical practice and research.
Objective:
We aimed to identify measures of dual-task costs of gait and turning that best discriminate performance in people with PD from healthy individuals. We also investigated the relationship between the most discriminative measures of dual-task costs of gait and turning with disease severity and disease duration.
Methods:
People with mild-to-moderate PD (n = 144) and age-matched healthy individuals (n = 79) wore 8 inertial sensors while walking under single and dual-task (reciting every other letter of the alphabet) conditions. Outcome measures included 26 objective measures within four gait domains (upper/lower body, turning and variability). The area under the curve (AUC) from the receiver-operator characteristic plot was calculated to compare discriminative ability of dual-task costs on gait across outcome measures.
Results:
PD-specific, dual-task interference was identified for arm range of motion, foot strike angle, turn velocity and turn duration. Arm range of motion (AUC = 0.73) and foot strike angle (AUC = 0.68) had the largest AUCs across dual-task costs measures and they were associated with disease severity and/or disease duration. In contrast, the most commonly used dual-task gait measure, gait speed, showed an AUC of only 0.54.
Conclusion:
Findings suggest that people with PD rely more than healthy individuals on executive-attentional resources to control arm swing, foot strike, and turning, but not gait speed. The dual-task costs of arm range of motion best discriminated people with PD from healthy individuals.
Background: Functional near-infrared spectroscopy (fNIRS) is increasingly used in the field of posture and gait to investigate patterns of cortical brain activation while people move freely. fNIRS methods, analysis and reporting of data vary greatly across studies which in turn can limit the replication of research, interpretation of findings and comparison across works.
Research question and methods: Considering these issues, we propose a set of practical recommendations for the conduct and reporting of fNIRS studies in posture and gait, acknowledging specific challenges related to clinical groups with posture and gait disorders.
Results: Our paper is organized around three main sections: 1) hardware set up and study protocols, 2) artefact removal and data processing and, 3) outcome measures, validity and reliability; it is supplemented with a detailed checklist.
Significance: This paper was written by a core group of members of the International Society for Posture and Gait Research and posture and gait researchers, all experienced in fNIRS research, with the intent of assisting the research community to lead innovative and impactful fNIRS studies in the field of posture and gait, whilst ensuring standardization of research.
Background
Declines in gait parameters are common with aging and more pronounced in tasks with increased executive demand. However, the neural correlates of age-related gait impairments are not fully understood yet.
Objectives
To investigate ( a) the effects of aging on prefrontal cortex (PFC) activity and gait parameters during usual walking, obstacle crossing and dual-task walking and ( b) the association between PFC activity and measures of gait and executive function.
Methods
Eighty-eight healthy individuals were distributed into 6 age-groups: 20-25 (G20), 30-35 (G30), 40-45 (G40), 50-55 (G50), 60-65 (G60), and 70-75 years (G70). Participants walked overground under 3 conditions: usual walking, obstacle crossing, and dual-task walking. Changes in oxygenated and deoxygenated hemoglobin in the PFC were recorded using functional near-infrared spectroscopy. Gait spatiotemporal parameters were assessed using an electronic walkway. Executive function was assessed through validated tests.
Results
Between-group differences on PFC activity were observed for all conditions. Multiple groups (ie, G30, G50, G60, and G70) showed increased PFC activity in at least one of the walking conditions. Young adults (G20 and G30) had the lowest levels of PFC activity while G60 had the highest levels. Only G70 showed reduced executive function and gait impairments (which were more pronounced during obstacle crossing and dual-task walking). PFC activity was related to gait and executive function.
Conclusions
Aging causes a gradual increase in PFC activity during walking. This compensatory mechanism may reach the resource ceiling in the 70s, when reduced executive function limits its efficiency and gait impairments are observed.
Background
Concussion may result in acutely impaired dynamic balance control that can persist up to two months post injury. Such impairment has been detected using sophisticated whole body center of mass kinematic metrics derived from camera-based motion analysis under a dual-task paradigm. However, wearable sensor kinematics for describing gait imbalance is lacking.
Methods
This study employed a longitudinal design. Gait balance control of acutely concussed and healthy matched control participants was assessed at five post-injury time points (within 72 h of injury, at one week, two weeks, one month, and two months). Tri-axial accelerations and angular velocities were collected with a dual-task gait protocol using an inertial measurement unit placed over the fifth lumbar vertebra.
Findings
Eight consistent gait event specific peak accelerations and six peak angular velocities measured by the inertial measurement unit were examined. Peak yaw and roll angular velocities at heel strike and peak roll angular velocities during early single-support, distinguished healthy from concussed participants across the two month post-injury period, while peak vertical acceleration at the end of terminal stance peak medial-lateral acceleration to the right during loading response showed promise.
Interpretation
Utilization of peak accelerations and angular velocities collected from a single inertial measurement unit placed over the fifth lumbar vertebra in a divided attention paradigm may offer a clinically feasible method for detecting subtle changes in gait balance control in concussed individuals.
Background:
Tripping and falling are common at work. Investigating the perceived risk of tripping is important for the safety of workers.
Objective:
To test the hypotheses that the perceived risk of tripping is affected by obstacle depth, obstacle height, number of obstacle, and light location under dimmed lighting conditions.
Methods:
A walkway with one to three obstacles in the middle was prepared. Each obstacle had a height of 0, 5, or 10 cm and a depth of 1 or 10 cm. The laboratory was dimmed with only one light either at the beginning, the midway, or at the end of the walkway. The perceived risk of tripping (PRT) was measured both before and after the participant walked through the walkway. A rating of gait disturbance (RGD) to each participant upon crossing the obstacle was also recorded.
Results:
The PRT measured both before and after the walk were between "almost no" to "medium" risk levels. The RGD was affected significantly by the location of the light, obstacle height, obstacle depth, and number of obstacle.
Conclusion:
The location of light significantly affected the PRT both before and after the participants walked. The participants perceived a higher risk of tripping and had a relative high probability of foot-obstacle contact when the light was behind than when the light was in the front.
Objectives
Degradation of striatal dopamine in Parkinson’s disease (PD) may initially be supplemented by increased cognitive control mediated by cholinergic mechanisms. Shift to cognitive control of walking can be quantified by prefrontal cortex (PFC) activation. Levodopa improves certain aspects of gait and worsens others, and cholinergic augmentation influence on gait and PFC activity remains unclear. This study examined dopaminergic and cholinergic influence on gait and PFC activity while walking in PD.
Methods
A single-site, randomized, double-blind, cross-over trial examined effects of levodopa and donepezil in PD. 20 PD participants were randomized and 19 completed the trial. Participants were randomized to either levodopa+donepezil (5mg) or levodopa+placebo treatments, with two-weeks with treatment and a two-week washout. The primary outcome was change in PFC activity while walking, and secondary outcomes were change in gait, dual-task performance and attention.
Results
Levodopa decreased PFC activity compared to Off medication (effect size: -0.51), whereas the addition of donepezil reversed this decrease. Gait speed and stride length, under single and dual-task conditions, improved with combined donepezil and levodopa compared to Off medication (effect size: 1 for gait speed and 0.75 for stride length). Dual-task reaction time was quicker with levodopa compared to Off medication (effect size: -0.87), and accuracy improved with combined donepezil and levodopa (effect size: 0.47).
Conclusions
Cholinergic therapy, specifically donepezil 5mg/day for two-weeks, can alter PFC activity when walking, and improve secondary cognitive task accuracy and gait in PD. Further studies will investigate whether higher PFC activity while walking is associated with gait changes.
Objective
Asymmetric walking after stroke is common, detrimental, and difficult to treat, but current knowledge of underlying physiological mechanisms is limited. This study investigated electromyographic (EMG) features of temporal gait asymmetry (TGA).
Methods
Participants post-stroke with or without TGA and control adults (n=27, 8, and 9, respectively) performed self-paced overground gait trials. EMG, force plate, and motion capture data were collected. Lower limb muscle activity was compared across groups and sides (more/less affected).
Results
Significant group by side interaction effects were found: more affected plantarflexor stance activity ended early (p=.0006) and less affected dorsiflexor on/off time was delayed (p<.01) in persons with asymmetry compared to symmetric and normative controls. The TGA group exhibited fewer dorsiflexor bursts during swing (p=.0009).
Conclusions
Temporal patterns of muscular activation, particularly about the ankle around the stance-to-swing transition period, are associated with TGA. The results may reflect specific impairments or compensations that affect locomotor coordination.
Significance
Neuromuscular underpinnings of spatiotemporal asymmetry have not been previously characterized. These novel findings may inform targeted therapeutic strategies to improve gait quality after stroke.
Background. Although dopaminergic medication improves dual task walking in people with Parkinson disease (PD), the underlying neural mechanisms are not yet fully understood. As prefrontal cognitive resources are involved in dual task walking, evaluation of the prefrontal cortex (PFC) is required. Objective. To investigate the effect of dopaminergic medication on PFC activity and gait parameters during dual task walking in people with PD. Methods. A total of 20 individuals with PD (69.8 ± 5.9 years) and 30 healthy older people (68.0 ± 5.6 years) performed 2 walking conditions: single and dual task (walking while performing a digit vigilance task). A mobile functional near infrared spectroscopy system and an electronic sensor carpet were used to analyze PFC activation and gait parameters, respectively. Relative concentrations of oxygenated hemoglobin (HbO 2 ) from the left and right PFC were measured. Results. People with PD in the off state did not present changes in HbO 2 level in the left PFC across walking conditions. In contrast, in the on state, they presented increased HbO 2 levels during dual task compared with single task. Regardless of medication state, people with PD presented increased HbO 2 levels in the right PFC during dual task walking compared with single task. The control group demonstrated increased PFC activity in both hemispheres during dual task compared with single task. People with PD showed increases in both step length and velocity in the on state compared with the off state. Conclusions. PD limits the activation of the left PFC during dual task walking, and dopaminergic medication facilitates its recruitment.
The Sports Concussion Assessment Tool (SCAT) is a pen and paper-based evaluation tool for use by healthcare professionals in acute evaluation of suspected concussion. Here we present a feasibility study towards instrumented SCAT (iSCAT). Traditionally, a healthcare professional counts the number of errors according to SCAT marking criteria matrix. Due to the subjective nature of the SCAT, it is hypothesized that the instrumented version of the test will be more accurate while providing additional digital-based parameters to better inform player management. The feasibility study focuses on the SCAT physical functioning assessments: double leg stance, single-leg stance, tandem stance, and tandem gait. Amateur rugby players underwent iSCAT testing and data were recorded with 8 inertial sensors attached at different anatomical locations. Video data were gathered simultaneously for a reference standard. An iSCAT algorithm was used to detect errors and quantify additional concussion-based time and frequency domain parameters to assess participant stability during balance and gait tests. Future work aims to instrument additional SCAT features such as hand-eye coordination while deploying methods within a larger concussion project.
Mobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. These differences appear to be affected by psychological, physiological, cognitive, environmental, and technical factors, and by the types of mobilities and diagnoses assessed. To facilitate the successful adaptation of the unsupervised assessment of mobility into clinical practice and clinical trials, clinicians and researchers should consider these disparities and the multiple factors that contribute to them.
Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to facilitate continuous fall risk assessment in non-hospital settings. However, a gait analysis system is not sufficient to detect the characteristics leading to increased fall risk, and powerful inference models are required to detect the underlying factors specific to fall risk. Machine learning models and especially the recently proposed deep learning methods offer the needed predictive power. Deep neural networks have the potential to produce models that can operate directly on the raw data, thus alleviating the need for feature engineering. However, the domain knowledge inherent in the well-established spatio-temporal gait parameters are still valuable to help a model achieve high inference accuracies. In this study, we explore deep learning methods, specifically long short-term memory (LSTM) neural networks, for the problem of fall risk assessment. We utilize sequences of spatio-temporal gait parameters extracted by an inertial sensor-based gait analysis system as input features. To quantify the performance of the proposed approach, we compare it with more traditional machine learning methods. The proposed LSTM model, trained with a gait dataset collected from 60 neurological disorder patients, achieves a superior classification accuracy of 92.1% on a separate test dataset collected from 16 patients. This study serves as one of the first attempts to employ deep learning approaches in this domain and the results demonstrate their potential.
Introduction
Mild traumatic brain injury (mTBI) can impact gait, with deficits linked to underlying neural disturbances in cognitive, motor and sensory systems. Gait is complex as it is comprised of multiple characteristics that are sensitive to underlying neural deficits. However, there is currently no clear framework to guide selection of gait characteristics in mTBI. This study developed a model of gait in chronic mTBI and replicated this in a separate group of controls, to provide a comprehensive and structured methodology on which to base gait assessment and analysis.
Methods
Fifty-two people with chronic mTBI and 59 controls completed a controlled laboratory gait assessment; walking for two minutes back and forth over a 13m distance while wearing five wirelessly synchronized inertial sensors. Thirteen gait characteristics derived from the inertial sensors were selected for entry into the principle component analysis based on previous literature, robustness and novelty. Principle component analysis was then used to derive domains (components) of gait.
Results
Four gait domains were derived for our chronic mTBI group (variability, rhythm, pace and turning) and this was replicated in a separate control cohort. Domains totaled 80.8% and 77.4% of variance in gait for chronic mTBI and controls, respectively. Gait characteristic loading was unambiguous for all features, with the exception of gait speed in controls that loaded on pace and rhythm domains.
Conclusion
This study contributes a four component model of gait in chronic mTBI and controls that can be used to comprehensively assess and analyze gait and underlying mechanisms involved in impairment, or examine the influence of interventions.
Development and in-vivo validation of a Continuous Wave (CW) functional Near Infrared Spectroscopy (fNIRS) system is presented. The system is wearable, fiber-less, multi-channel (16×16, 256 channels) and expandable and it relies on silicon photomultipliers (SiPMs) for light detection. SiPMs are inexpensive, low voltage and resilient semiconductor light detectors, whose performances are analogous to photomultiplier tubes (PMTs). The advantage of SiPMs with respect to PMTs is that they allow direct contact with the scalp and avoidance of optical fibers. In fact, the coupling of SiPMs and light emitting diodes (LEDs) allows the transfer of the analog signals to and from the scalp through thin electric cables that greatly increase the system flexibility. Moreover, the optical probes, mechanically resembling electroencephalographic electrodes, are robust against motion artifacts. In order to increase the signal-to-noise-ratio (SNR) of the fNIRS acquisition and to decrease ambient noise contamination, a digital lock-in technique was implemented through LEDs modulation and SiPMs signal processing chain. In-vivo validation proved the system capabilities of detecting functional brain activity in the sensorimotor cortices. When compared to other state-of-the-art wearable fNIRS systems, the single photon sensitivity and dynamic range of SiPMs can exploit the long and variable interoptode distances needed for estimation of brain functional hemodynamics using CW-fNIRS.
Introduction:
Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis.
Methods:
A cohort of 46 subjects (divided into three groups) affected by PD patients at different stages and PSP patients was acquired through gait analysis and spatial and temporal parameters were analysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross-validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented.
Results:
Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group.
Conclusion:
The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.
