The manumeter: a wearable device for monitoring daily use of the wrist and fingers

A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability

OBJECTIVE

To establish the concurrent validity, acceptability, and sensor optimization of a consumer-grade, wearable, multi-sensor system to capture quantity and quality metrics of mobility and upper limb movements in stroke survivors.

DESIGN

Single-session, cross-sectional.

SETTING

Clinical research laboratory.

PARTICIPANTS

Thirty chronic stroke survivors (age 57 (10) years; 33% female) with mild to severe motor impairments participated.

INTERVENTIONS

Not Applicable.

MAIN OUTCOME MEASURES

Participants donned 5 sensors and performed standardized assessments of mobility and upper limb (UL) movement. True/false, positive/negative time in active movement for the UL were calculated and compared to criterion-standards using an accuracy rate. Bland-Altman plots and linear regression models were used to establish concurrent validity of UL movement counts, step counts, and stance time symmetry of MiGo against established criterion-standard measures. Acceptability and sensor optimization were assessed through an end-user survey and decision matrix.

RESULTS

Mobility metrics showed excellent association with criterion-standards for step counts (video: r=0.988, P<.001, IMU: r=0.921, P<.001) and stance-time symmetry (r=0.722, P<.001). In the UL, movement counts showed excellent to good agreement (paretic: r=0.849, P<.001, nonparetic: r=0.672, P<.001). Accuracy of active movement time was 85.2% (paretic) and 88.0% (nonparetic) UL. Most participants (63.3%) had difficulty donning/doffing the sensors. Acceptability was high (4.2/5).

CONCLUSIONS

The sensors demonstrated excellent concurrent validity for mobility metrics and UL movements of stroke survivors. Acceptability of the system was high, but alternative wristbands should be considered.

Designing an Egocentric Video-Based Dashboard to Report Hand Performance Measures for Outpatient Rehabilitation of Cervical Spinal Cord Injury

Background

Functional use of the upper extremities (UEs) is a top recovery priority for individuals with cervical spinal cord injury (cSCI), but the inability to monitor recovery at home and limitations in hand function outcome measures impede optimal recovery.

Objectives

We developed a framework using wearable cameras to monitor hand use at home and aimed to identify the best way to report information to clinicians.

Methods

A dashboard was iteratively developed with clinician (n = 7) input through focus groups and interviews, creating low-fidelity prototypes based on recurring feedback until no new information emerged. Affinity diagramming was used to identify themes and subthemes from interview data. User stories were developed and mapped to specific features to create a high-fidelity prototype.

Results

Useful elements identified for a dashboard reporting hand performance included summaries to interpret graphs, a breakdown of hand posture and activity to provide context, video snippets to qualitatively view hand use at home, patient notes to understand patient satisfaction or struggles, and time series graphing of metrics to measure trends over time.

Conclusion

Involving end-users in the design process and breaking down user requirements into user stories helped identify necessary interface elements for reporting hand performance metrics to clinicians. Clinicians recognized the dashboard's potential to monitor rehabilitation progress, provide feedback on hand use, and track progress over time. Concerns were raised about the implementation into clinical practice, therefore further inquiry is needed to determine the tool's feasibility and usefulness in clinical practice for individuals with UE impairments.

Recognizing hand use and hand role at home after stroke from egocentric video

Hand function is a central determinant of independence after stroke. Measuring hand use in the home environment is necessary to evaluate the impact of new interventions, and calls for novel wearable technologies. Egocentric video can capture hand-object interactions in context, as well as show how more-affected hands are used during bilateral tasks (for stabilization or manipulation). Automated methods are required to extract this information. The objective of this study was to use artificial intelligence-based computer vision to classify hand use and hand role from egocentric videos recorded at home after stroke. Twenty-one stroke survivors participated in the study. A random forest classifier, a SlowFast neural network, and the Hand Object Detector neural network were applied to identify hand use and hand role at home. Leave-One-Subject-Out-Cross-Validation (LOSOCV) was used to evaluate the performance of the three models. Between-group differences of the models were calculated based on the Mathews correlation coefficient (MCC). For hand use detection, the Hand Object Detector had significantly higher performance than the other models. The macro average MCCs using this model in the LOSOCV were 0.50 ± 0.23 for the more-affected hands and 0.58 ± 0.18 for the less-affected hands. Hand role classification had macro average MCCs in the LOSOCV that were close to zero for all models. Using egocentric video to capture the hand use of stroke survivors at home is technically feasible. Pose estimation to track finger movements may be beneficial to classifying hand roles in the future.

Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call "Hand Activity Recognition through using a Convolutional neural network with Spectrograms" (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R² = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.

Quantitative measurement of finger usage in stroke hemiplegia using ring-shaped wearable devices

BACKGROUND

In post-stroke rehabilitation, positive use of affected limbs in daily life is important to improve affected upper-limb function. Several studies have quantitatively evaluated the amount of upper-limb activity, but few have measured finger usage. In this study, we used a ring-shaped wearable device to measure upper-limb and finger usage simultaneously in hospitalized patients with hemiplegic stroke and investigated the association between finger usage and general clinical evaluation.

METHODS

Twenty patients with hemiplegic stroke in an inpatient hospital participated in this study. All patients wore a ring-shaped wearable device on both hands for 9 h on the day of the intervention, and their finger and upper-limb usage were recorded. For the rehabilitation outcome assessments, the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Simple Test for Evaluating Hand Function (STEF), Action Research Arm Test (ARAT), Motor Activity Log-14 (MAL), and Functional Independence Measure Motor (FIM-m) were performed and evaluated on the same day as the intervention.

RESULTS

Finger usage of the affected hand was moderately correlated with STEF ([Formula: see text], [Formula: see text]) and STEF ratio ([Formula: see text], [Formula: see text]). The finger-usage ratio was moderately correlated with FMA-UE ([Formula: see text], [Formula: see text]) and ARAT ([Formula: see text], [Formula: see text]), and strongly correlated with STEF ([Formula: see text], [Formula: see text]) and STEF ratio ([Formula: see text], [Formula: see text]). The upper-limb usage of the affected side was moderately correlated with FMA-UE ([Formula: see text], [Formula: see text]), STEF ([Formula: see text], [Formula: see text]) and STEF ratio ([Formula: see text], [Formula: see text]), and strongly correlated with ARAT ([Formula: see text], [Formula: see text]). The upper-limb usage ratio was moderately correlated with ARAT ([Formula: see text], [Formula: see text]) and STEF ([Formula: see text], [Formula: see text]), and strongly correlated with the STEF ratio ([Formula: see text], [Formula: see text]). By contrast, there was no correlation between MAL and any of the measurements.

CONCLUSIONS

This measurement technique provided useful information that was not biased by the subjectivity of the patients and therapists.

Optimized Home Rehabilitation Technology Reduces Upper Extremity Impairment Compared to a Conventional Home Exercise Program: A Randomized, Controlled, Single-Blind Trial in Subacute Stroke

BACKGROUND

Upper extremity (UE) stroke rehabilitation requires patients to perform exercises at home, yet patients show limited benefit from paper-based home exercise programs.

OBJECTIVE

To compare the effectiveness of 2 home exercise programs for reducing UE impairment: a paper-based approach and a sensorized exercise system that incorporates recommended design features for home rehabilitation technology.

METHODS

In this single-blind, randomized controlled trial, 27 participants in the subacute phase of stroke were assigned to the sensorized exercise (n = 14) or conventional therapy group (n = 13), though 2 participants in the conventional therapy group were lost to follow-up. Participants were instructed to perform self-guided movement training at home for at least 3 hours/week for 3 consecutive weeks. The sensorized exercise group used FitMi, a computer game with 2 puck-like sensors that encourages movement intensity and auto-progresses users through 40 exercises. The conventional group used a paper book of exercises. The primary outcome measure was the change in Upper Extremity Fugl-Meyer (UEFM) score from baseline to follow-up. Secondary measures included the Modified Ashworth Scale for spasticity (MAS) and the Visual Analog Pain (VAP) scale.

RESULTS

Participants who used FitMi improved by an average of 8.0 ± 4.6 points on the UEFM scale compared to 3.0 ± 6.1 points for the conventional participants, a significant difference (t-test, P = .029). FitMi participants exhibited no significant changes in UE MAS or VAP scores.

CONCLUSIONS

A sensor-based exercise system incorporating a suite of recommended design features significantly and safely reduced UE impairment compared to a paper-based, home exercise program.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03503617.

Comparing algorithms for assessing upper limb use with inertial measurement units

The various existing measures to quantify upper limb use from wrist-worn inertial measurement units can be grouped into three categories: 1) Thresholded activity counting, 2) Gross movement score and 3) machine learning. However, there is currently no direct comparison of all these measures on a single dataset. While machine learning is a promising approach to detecting upper limb use, there is currently no knowledge of the information used by machine learning measures and the data-related factors that influence their performance. The current study conducted a direct comparison of the 1) thresholded activity counting measures, 2) gross movement score,3) a hybrid activity counting and gross movement score measure (introduced in this study), and 4) machine learning measures for detecting upper-limb use, using previously collected data. Two additional analyses were also performed to understand the nature of the information used by machine learning measures and the influence of data on the performance of machine learning measures. The intra-subject random forest machine learning measure detected upper limb use more accurately than all other measures, confirming previous observations in the literature. Among the non-machine learning (or traditional) algorithms, the hybrid activity counting and gross movement score measure performed better than the other measures. Further analysis of the random forest measure revealed that this measure used information about the forearm's orientation and amount of movement to detect upper limb use. The performance of machine learning measures was influenced by the types of movements and the proportion of functional data in the training/testing datasets. The study outcomes show that machine learning measures perform better than traditional measures and shed some light on how these methods detect upper-limb use. However, in the absence of annotated data for training machine learning measures, the hybrid activity counting and gross movement score measure presents a reasonable alternative. We believe this paper presents a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors.

Measuring Hand Use in the Home after Cervical Spinal Cord Injury Using Egocentric Video

Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. The aim of this study was to develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 h of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc); the average duration of individual interactions (Dur); and the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension [GRASSP], Upper Extremity Motor Score [UEMS], and Spinal Cord Independent Measure [SCIM]). Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use.

Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives

BACKGROUND

The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice.

OBJECTIVE

We aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders.

METHODS

We conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach.

RESULTS

Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice.

CONCLUSIONS

These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.

Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.

Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke

Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke. A force myography (FMG), electromyography (EMG), and inertial measurement unit (IMU)-based multi-sensor fusion model was proposed for hand movement classification, which was worn on the user’s affected arm. Two movement recognition-based serious games were developed for hand movement and cognition training. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed experiments while playing interactive serious games requiring 12 activities-of-daily-living (ADLs) hand movements taken from the Fugl Meyer Assessment. Feasibility was evaluated by movement classification accuracy and qualitative patient questionnaires. The offline classification accuracy using combined FMG-EMG-IMU was 81.0% for the 12 movements, which was significantly higher than any single sensing modality; only EMG, only FMG, and only IMU were 69.6, 63.2, and 47.8%, respectively. Patients reported that they were more enthusiastic about hand movement training while playing the serious games as compared to conventional methods and strongly agreed that they subjectively felt that the proposed training could be beneficial for improving upper limb motor function. These results showed that multimodal-sensor fusion improved hand gesture classification accuracy for stroke patients and demonstrated the potential of this proposed approach to be used as upper limb movement training after stroke.

Virtuous and Vicious Cycles of Arm Use and Function Post-stroke

Large doses of movement practice have been shown to restore upper extremities' motor function in a significant subset of individuals post-stroke. However, such large doses are both difficult to implement in the clinic and highly inefficient. In addition, an important reduction in upper extremity function and use is commonly seen following rehabilitation-induced gains, resulting in "rehabilitation in vain". For those with mild to moderate sensorimotor impairment, the limited spontaneous use of the more affected limb during activities of daily living has been previously proposed to cause a decline of motor function, initiating a vicious cycle of recovery, in which non-use and poor performance reinforce each other. Here, we review computational, experimental, and clinical studies that support the view that if arm use is raised above an effective threshold, one enters a virtuous cycle in which arm use and function can reinforce each other via self-practice in the wild. If not, one enters a vicious cycle of declining arm use and function. In turn, and in line with best practice therapy recommendations, this virtuous/vicious cycle model advocates for a paradigm shift in neurorehabilitation whereby rehabilitation be embedded in activities of daily living such that self-practice with the aid of wearable technology that reminds and motivates can enhance paretic limb use of those who possess adequate residual sensorimotor capacity. Altogether, this model points to a user-centered approach to recovery post-stroke that is tailored to the participant's level of arm use and designed to motivate and engage in self-practice through progressive success in accomplishing meaningful activities in the wild.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

A tracking device for a wearable high-DOF passive hand exoskeleton

In previous work, we developed an exoskeleton (HandSOME II) that allows movement at 15 hand degrees of freedom (DOF) and is intended for take-home use. An activity tracking device was developed in order to track index finger movement with a pair of magnetometers and magnet. The goal was to detect grip attempts by the individual. Machine learning was utilized to estimate angles for metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints at the index finger. Testing was performed with healthy control and individuals with stroke.Clinical Relevance- This device and method of data collection during daily activities might be useful for stroke rehabilitation and compliance with home-based therapy programs.

A Framework for Sensor-Based Assessment of Upper-Limb Functioning in Hemiparesis

The ultimate goal of any upper-limb neurorehabilitation procedure is to improve upper-limb functioning in daily life. While clinic-based assessments provide an assessment of what a patient can do, they do not completely reflect what a patient does in his/her daily life. The use of compensatory strategies such as the use of the less affected upper-limb or excessive use of trunk in daily life is a common behavioral pattern seen in patients with hemiparesis. To this end, there has been an increasing interest in the use of wearable sensors to objectively assess upper-limb functioning. This paper presents a framework for assessing upper-limb functioning using sensors by providing: (a) a set of definitions of important constructs associated with upper-limb functioning; (b) different visualization methods for evaluating upper-limb functioning; and (c) two new measures for quantifying how much an upper-limb is used and the relative bias in their use. The demonstration of some of these components is presented using data collected from inertial measurement units from a previous study. The proposed framework can help guide the future technical and clinical work in this area to realize valid, objective, and robust tools for assessing upper-limb functioning. This will in turn drive the refinement and standardization of the assessment of upper-limb functioning.

Circulating Nurse Assistant: Non-Contact Body Centric Gesture Recognition Towards Reducing Latrogenic Contamination

Iatrogenic contamination causes serious health threats to both patients and healthcare staff. Contact operation is an important transmission route for nosocomial infection. Reducing direct contact during medical treatment can reduce nosocomial infection quickly and effectively. Scientific and technological progress in the 5G era brings new solutions to the problem of iatrogenic contamination. We conducted experiments at 27 GHz and 37 GHz to achieve contactless gesture recognition through the bornprint of body centric channel. The original channel S-parameters can achieve 82% (27 GHz) and 89% (37 GHz) basic recognition accuracy through simple statistical analysis. Basic switch recognition and multi-gesture selection recognition can meet the common operation requirements of circulating nurses, greatly reducing contact operations and reducing the probability of cross-contamination. Fully physically isolated body centric channel gesture sensing provides a new entry point for reducing iatrogenic contamination.

Perspectives and recommendations of individuals with tetraplegia regarding wearable cameras for monitoring hand function at home: Insights from a community-based study

CONTEXT

Wearable cameras have great potential for producing novel outcome measures of upper limb (UL) function and guiding care in individuals with cervical spinal cord injury (cSCI) living in the community. However, little is known about the perspectives of individuals with cSCI on the potential adoption of this technology.

OBJECTIVE

To analyze feedback from individuals with cSCI regarding the use of wearable cameras to record daily activities at home, in order to define guidelines for improving the design of this technology and fostering its implementation to optimize UL rehabilitation.

DESIGN

Mixed-methods study.

PARTICIPANTS

Thirteen adults with cSCI C3-C8 AIS A-D impairment.

MEASURES

Interview including survey and semi-structured questions.

RESULTS

Participants felt that this technology can provide naturalistic information regarding hand use to clinicians and researchers, which in turn can lead to better assessments of UL function and optimized therapies. Participants described the technology as easy-to-use but often reported discomfort that prevented them from conducting long recordings of fully natural activities. Privacy concerns included the possibility to capture household members and personal information displayed on objects (e.g. smartphones).

CONCLUSION

We provide the first set of guidelines to help researchers and therapists understand which steps need to be taken to translate wearable cameras into outpatient care and community-based research for UL rehabilitation. These guidelines include miniaturized and easy-to-wear cameras, as well as multiple measures for preventing privacy concerns such as avoiding public spaces and providing control over the recordings (e.g. start and stop the recordings at any time, keep or delete a recording).

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

Identifying Hand Use and Hand Roles After Stroke Using Egocentric Video

Objective: Upper limb (UL) impairment impacts quality of life, but is common after stroke. UL function evaluated in the clinic may not reflect use in activities of daily living (ADLs) after stroke, and current approaches for assessment at home rely on self-report and lack details about hand function. Wrist-worn accelerometers have been applied to capture UL use, but also fail to reveal details of hand function. In response, a wearable system is proposed consisting of egocentric cameras combined with computer vision approaches, in order to identify hand use (hand-object interactions) and the role of the more-affected hand (as stabilizer or manipulator) in unconstrained environments. Methods: Nine stroke survivors recorded their performance of ADLs in a home simulation laboratory using an egocentric camera. Motion, hand shape, colour, and hand size change features were generated and fed into random forest classifiers to detect hand use and classify hand roles. Leave-one-subject-out cross-validation (LOSOCV) and leave-one-task-out cross-validation (LOTOCV) were used to evaluate the robustness of the algorithms. Results: LOSOCV and LOTOCV F1-scores for more-affected hand use were 0.64 ± 0.24 and 0.76 ± 0.23, respectively. For less-affected hands, LOSOCV and LOTOCV F1-scores were 0.72 ± 0.20 and 0.82 ± 0.22. F1-scores for hand role classification were 0.70 ± 0.19 and 0.68 ± 0.23 in the more-affected hand for LOSOCV and LOTOCV, respectively, and 0.59 ± 0.23 and 0.65 ± 0.28 in the less-affected hand. Conclusion: The results demonstrate the feasibility of predicting hand use and the hand roles of stroke survivors from egocentric videos.

Visualising the knowledge structure and evolution of wearable device research

In recent years, the literature associated with wearable devices has grown rapidly, but few studies have used bibliometrics and a visualisation approach to conduct deep mining and reveal a panorama of the wearable devices field. To explore the foundational knowledge and research hotspots of the wearable devices field, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) The subject evolution path includes sensor research, sensitivity research and multi-functional device research. (b) Wearable device research focuses on information collection, sensor materials, manufacturing technology and application, artificial intelligence technology application, energy supply and medical applications. The future development trend will be further studied in combination with big data analysis, telemedicine and personalised precision medical application.

Magnetically Counting Hand Movements: Validation of a Calibration-Free Algorithm and Application to Testing the Threshold Hypothesis of Real-World Hand Use after Stroke

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the “Manumeter”). The “HAND” (Hand Activity estimated by Nonlinear Detection) algorithm assigns a “HAND count” by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants’ Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method

BACKGROUND

Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.

OBJECTIVE

Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity.

METHODS

Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb.

RESULTS

The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth.

CONCLUSIONS

In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

Wearable sensing devices for upper limbs: A systematic review

Wearable sensing devices, which are smart electronic devices that can be worn on the body as implants or accessories, have attracted much research interest in recent years. They are rapidly advancing in terms of technology, functionality, size, and real-time applications along with the fast development of manufacturing technologies and sensor technologies. By covering some of the most important technologies and algorithms of wearable devices, this paper is intended to provide an overview of upper-limb wearable device research and to explore future research trends. The review of the state-of-the-art of upper-limb wearable technologies involving wearable design, sensor technologies, wearable computing algorithms and wearable applications is presented along with a summary of their advantages and disadvantages. Toward the end of this paper, we highlight areas of future research potential. It is our goal that this review will guide future researchers to develop better wearable sensing devices for upper limbs.

A wearable vision-based system for detecting hand-object interactions in individuals with cervical spinal cord injury: First results in the home environment

Cervical spinal cord injury (cSCI) causes the paralysis of upper and lower limbs and trunk, significantly reducing quality of life and community participation of the affected individuals. The functional use of the upper limbs is the top recovery priority of people with cSCI and wearable vision-based systems have recently been proposed to extract objective outcome measures that reflect hand function in a natural context. However, previous studies were conducted in a controlled environment and may not be indicative of the actual hand use of people with cSCI living in the community. Thus, we propose a deep learning algorithm for automatically detecting hand-object interactions in egocentric videos recorded by participants with cSCI during their daily activities at home. The proposed approach is able to detect hand-object interactions with good accuracy (F1-score up to 0.82), demonstrating the feasibility of this system in uncontrolled situations (e.g., unscripted activities and variable illumination). This result paves the way for the development of an automated tool for measuring hand function in people with cSCI living in the community.

Progress on Range of Motion After Total Knee Replacement by Sensor-Based System

For total knee replacement (TKR) patients, rehabilitation after the surgery is key toregaining mobility. This study proposes a sensor-based system for effectively monitoringrehabilitation progress after TKR. The system comprises a hardware module consisting of thetriaxial accelerometer and gyroscope, a microcontroller, and a Bluetooth module, and a softwareapp for monitoring the motion of the knee joint. Three indices, namely the number of swings, themaximum knee flexion angle, and the duration of practice each time, were used as metrics tomeasure the knee rehabilitation progress. The proposed sensor device has advantages such asusability without spatiotemporal constraints and accuracy in monitoring the rehabilitation progress.The performance of the proposed system was compared with the measured range of motion of theCybex isokinetic dynamometer (or Cybex) professional rehabilitation equipment, and the resultsrevealed that the average absolute errors of the measured angles were between 1.65° and 3.27° forthe TKR subjects, depending on the swing speed. Experimental results verified that the proposedsystem is effective and comparable with the professional equipment.

Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors

Maximizing the motor practice in stroke survivors' living environments may significantly improve the functional recovery of their stroke-affected upper-limb. A wearable system that can continuously monitor upper-limb performance has been considered as an effective clinical solution for its potential to provide patient-centered, data-driven feedback to improve the motor dosage. Towards that end, we investigate a system leveraging a pair of finger-worn, ring-type accelerometers capable of monitoring both gross-arm and fine-hand movements that are clinically relevant to the performance of daily activities. In this work, we conduct a mixed-methods study to (1) quantitatively evaluate the efficacy of finger-worn accelerometers in measuring clinically relevant information regarding stroke survivors' upper-limb performance, and (2) qualitatively investigate design requirements for the self-monitoring system, based on data collected from 25 stroke survivors and seven occupational therapists. Our quantitative findings demonstrate strong face and convergent validity of the finger-worn accelerometers, and its responsiveness to changes in motor behavior. Our qualitative findings provide a detailed account of the current rehabilitation process while highlighting several challenges that therapists and stroke survivors face. This study offers promising directions for the design of a self-monitoring system that can encourage the affected limb use during stroke survivors' daily living.

Monitoring Methods of Human Body Joints: State-of-the-Art and Research Challenges

The world's population is aging: the expansion of the older adult population with multiple physical and health issues is now a huge socio-economic concern worldwide. Among these issues, the loss of mobility among older adults due to musculoskeletal disorders is especially serious as it has severe social, mental and physical consequences. Human body joint monitoring and early diagnosis of these disorders will be a strong and effective solution to this problem. A smart joint monitoring system can identify and record important musculoskeletal-related parameters. Such devices can be utilized for continuous monitoring of joint movements during the normal daily activities of older adults and the healing process of joints (hips, knees or ankles) during the post-surgery period. A viable monitoring system can be developed by combining miniaturized, durable, low-cost and compact sensors with the advanced communication technologies and data processing techniques. In this study, we have presented and compared different joint monitoring methods and sensing technologies recently reported. A discussion on sensors' data processing, interpretation, and analysis techniques is also presented. Finally, current research focus, as well as future prospects and development challenges in joint monitoring systems are discussed.

A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting

The use of wrist-worn accelerometers has recently gained tremendous interest among researchers and clinicians as an objective tool to quantify real-world use of the upper limbs during the performance of activities of daily living (ADLs). However, wrist-worn accelerometers have shown a number of limitations that hinder their adoption in the clinic. Among others, the inability of wrist-worn accelerometers to capture hand and finger movements is particularly relevant to monitoring the performance of ADLs. This study investigates the use of finger-worn accelerometers to capture both gross arm and fine hand movements for the assessment of real-world upper-limb use. A system of finger-worn accelerometers was utilized to monitor eighteen neurologically intact young adults while performing nine motor tasks in a laboratory setting. The system was also used to monitor eighteen subjects during the day time of a day in a free-living setting. A novel measure of real-world upper-limb function—comparing the duration of activities of the two limbs—was derived to identify which upper limb subjects predominantly used to perform ADLs. Two validated handedness self-reports, namely the Waterloo Handedness Questionnaire and the Fazio Laterality Inventory, were collected to assess convergent validity. The analysis of the data recorded in the laboratory showed that the proposed measure of upper-limb function is suitable to accurately detect unilateral vs. bilateral use of the upper limbs, including both gross arm movements and fine hand movements. When applied to recordings collected in a free-living setting, the proposed measure showed high correlation with self-reported handedness indices (i.e., ρ = 0.78 with the Waterloo Handedness Questionnaire scores and ρ = 0.77 with the Fazio Laterality Inventory scores). The results herein presented establish face and convergent validity of the proposed measure of real-world upper-limb function derived using data collected by means of finger-worn accelerometers.

Towards an IoT-based upper limb rehabilitation assessment system

Rehabilitation of stroke survivors has been increasing in importance in recent years with increase in the occurrence of stroke. However, current clinical classification assessment is time-consuming while the result is not accurate and varies across physicians. This paper introduces an IoT-based upper limb rehabilitation assessment system for stroke survivors based on wireless sensing sub-system, data cloud, computing cloud and software based on Android platform. The system can automatically perform objective assessment. It is designed for home rehabilitation as well as for the concept of graded rehabilitation therapy.

The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings

Objective assessment of stroke survivors' upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold. First, this paper introduces and validates a new benchmark measurement of the amount of hand use based on data recorded by a motion capture system, the gold standard for human movement analysis. Second, this paper introduces a machine learning-based analytic pipeline that estimates the amount of hand use using data obtained from the wearable sensors and validates its estimation performance against the aforementioned benchmark measurement. Based on data collected from 18 neurologically intact individuals performing 11 motor tasks resembling various activities of daily living, the analytic results presented herein show that our new benchmark measure is reliable and responsive, and that the proposed wearable system can yield an accurate estimation of the amount of hand use (normalized root mean square error of 0.11 and average Pearson correlation of 0.78). This study has the potential to open up new research and clinical opportunities for monitoring hand function in ambulatory settings, ultimately enabling evidence-based, patient-centered rehabilitation and healthcare.

Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Wearable sensing is a new tool for quantifying upper extremity (UE) rehabilitation after stroke. However, it is unclear whether it provides information beyond what is available through standard clinical assessments. To investigate this question, people with a chronic stroke (n=9) wore accelerometers on both wrists for 9 hours on a single day during their daily activities. We used principal components analysis (PCA) to characterize how novel kinematic measures of jerk and acceleration asymmetry, along with conventional measures of limb use asymmetry and clinical function, explained the behavioral variance of UE recovery across participants. The first PC explained 55% of the variance and described a strong correlation between standard clinical assessments and limb use asymmetry, as has been observed previously. The second PC explained a further 31% of the variance and described a strong correlation between bimanual magnitude and jerk asymmetry. Because of the nature of PCA, this second PC is mathematically orthogonal to the first and thus uncorrelated with the clinical assessments. Therefore, kinematic metrics obtainable from bimanual accelerometry, including bimanual jerk asymmetry, encoded additional information about UE recovery. One interpretation is that the first PC relates to "functional status" and the second to "movement quality". We also describe a new graphical format for presenting bimanual wrist accelerometry data that facilitates identification of asymmetries.

Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community

OBJECTIVE

Hand function impairment after cervical spinal cord injury (SCI) can significantly reduce independence. Unlike current hand function assessments, wearable camera systems could potentially measure functional hand usage at home, and thus benefit the development of neurorehabilitation strategies. The objective of this study was to understand the views of individuals with SCI on the use of wearable cameras to track neurorehabilitation progress and outcomes in the community.

DESIGN

Questionnaires.

SETTING

Home simulation laboratory.

PARTICIPANTS

Fifteen individuals with cervical SCI.

OUTCOME MEASURES

After using wearable cameras in the simulated home environment, participants completed custom questionnaires, comprising open-ended and structured questions.

RESULTS

Participants showed relatively low concerns related to data confidentiality when first-person videos are used by clinicians (1.93 ± 1.28 on a 5-point Likert scale) or researchers (2.00 ± 1.31). Storing only automatically extracted metrics reduced privacy concerns. Though participants reported moderate privacy concerns (2.53 ± 1.51) about wearing a camera in daily life due to certain sensitive situations (e.g. washrooms), they felt that information about their hand usage at home is useful for researchers (4.73 ± 0.59), clinicians (4.47 ± 0.83), and themselves (4.40 ± 0.83). Participants found the system moderately comfortable (3.27 ± 1.44), but expressed low desire to use it frequently (2.87 ± 1.36).

CONCLUSION

Despite some privacy and comfort concerns, participants believed that the information obtained would be useful. With appropriate strategies to minimize the data stored and recording duration, wearable cameras can be a well-accepted tool to track function in the home and community after SCI.

Development of a Wearable Controller for Gesture-Recognition-Based Applications Using Polyvinylidene Fluoride

This paper reports on a wearable gesture-based controller fabricated using the sensing capabilities of the flexible thin-film piezoelectric polymer polyvinylidene fluoride (PVDF) which is shown to repeatedly and accurately discern, in real time, between right and left hand gestures. The PVDF is affixed to a compression sleeve worn on the forearm to create a wearable device that is flexible, adaptable, and highly shape conforming. Forearm muscle movements, which drive hand motions, are detected by the PVDF which outputs its voltage signal to a developed microcontroller-based board and processed by an artificial neural network that was trained to recognize the generated voltage profile of right and left hand gestures. The PVDF has been spatially shaded (etched) in such a way as to increase sensitivity to expected deformations caused by the specific muscles employed in making the targeted right and left gestures. The device proves to be exceptionally accurate both when positioned as intended and when rotated and translated on the forearm.

Force Myography for Monitoring Grasping in Individuals with Stroke with Mild to Moderate Upper-Extremity Impairments: A Preliminary Investigation in a Controlled Environment

There is increasing research interest in technologies that can detect grasping, to encourage functional use of the hand as part of daily living, and thus promote upper-extremity motor recovery in individuals with stroke. Force myography (FMG) has been shown to be effective for providing biofeedback to improve fine motor function in structured rehabilitation settings, involving isolated repetitions of a single grasp type, elicited at a predictable time, without upper-extremity movements. The use of FMG, with machine learning techniques, to detect and distinguish between grasping and no grasping, continues to be an active area of research, in healthy individuals. The feasibility of classifying FMG for grasp detection in populations with upper-extremity impairments, in the presence of upper-extremity movements, as would be expected in daily living, has yet to be established. We explore the feasibility of FMG for this application by establishing and comparing (1) FMG-based grasp detection accuracy and (2) the amount of training data necessary for accurate grasp classification, in individuals with stroke and healthy individuals. FMG data were collected using a flexible forearm band, embedded with six force-sensitive resistors (FSRs). Eight participants with stroke, with mild to moderate upper-extremity impairments, and eight healthy participants performed 20 repetitions of three tasks that involved reaching, grasping, and moving an object in different planes of movement. A validation sensor was placed on the object to label data as corresponding to a grasp or no grasp. Grasp detection performance was evaluated using linear and non-linear classifiers. The effect of training set size on classification accuracy was also determined. FMG-based grasp detection demonstrated high accuracy of 92.2% (σ = 3.5%) for participants with stroke and 96.0% (σ = 1.6%) for healthy volunteers using a support vector machine (SVM). The use of a training set that was 50% the size of the testing set resulted in 91.7% (σ = 3.9%) accuracy for participants with stroke and 95.6% (σ = 1.6%) for healthy participants. These promising results indicate that FMG may be feasible for monitoring grasping, in the presence of upper-extremity movements, in individuals with stroke with mild to moderate upper-extremity impairments.

Continuous quantitative monitoring of physical activity in Parkinson's disease patients by using wearable devices: a case-control study

The objective of this study was to explore the feasibility of using wearable devices to quantitatively measure the daily activity in patients with Parkinson's disease (PD) and to monitor medication-induced motor fluctuations. In this case-controlled study, we used monitored daily movement function in 21 patients with Parkinson's disease and 20 healthy volunteers. We analyzed the exercise types and sleep duration in the two groups and evaluated the correlation between daily movement function and age, gender, education, disease duration, Hohn-Yahr stage, UPDRS-II score, UPDRS-III score, and levodopa dose. We also determined the amount of exercise performed by PD patients at 1 h after taking levodopa and at 1 h before the next dose. The type of activity, average speed, and sleep duration in patients were significantly lower in PD patients than in healthy controls (P < 0.05). One hour after taking levodopa, patients were significantly more active than 1 h before the next dose (P < 0.05).Correlation analysis showed that age, gender, education, disease duration, Hohn-Yahr stage, UPDRS-II and UPDRS-III scores, and dosage of levodopa do not correlate with the daily movement function (P > 0.05) in patients with Parkinson's disease. In the control group, age and education were associated with daily movement function (P < 0.05), while gender was unrelated (P > 0.05). Continuous monitoring of daily activity may be useful to reveal medication-induced motor fluctuations in Parkinson's disease. The daily movement function may depend on age and education, but not on other parameters.

Interactive wearable systems for upper body rehabilitation: a systematic review

Background

The development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies.

Method

A systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010–April 2016).

Results

Forty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial.

Conclusions

This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Wearable Sensors for Remote Health Monitoring

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

Wearable Sensors for Remote Health Monitoring

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices

In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM) algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR) and false rejection rate (FRR) based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN) classification, support vector machines (SVM), and naive Bayesian method (NBM) classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices.

How Therapists Use Visualizations of Upper Limb Movement Information From Stroke Patients: A Qualitative Study With Simulated Information

Background

Stroke is a leading cause of disability worldwide, with upper limb deficits affecting an estimated 30% to 60% of survivors. The effectiveness of upper limb rehabilitation relies on numerous factors, particularly patient compliance to home programs and exercises set by therapists. However, therapists lack objective information about their patients’ adherence to rehabilitation exercises as well as other uses of the affected arm and hand in everyday life outside the clinic. We developed a system that consists of wearable sensor technology to monitor a patient’s arm movement and a Web-based dashboard to visualize this information for therapists.

Objective

The aim of our study was to evaluate how therapists use upper limb movement information visualized on a dashboard to support the rehabilitation process.

Methods

An interactive dashboard prototype with simulated movement information was created and evaluated through a user-centered design process with therapists (N=8) at a rehabilitation clinic. Data were collected through observations of therapists interacting with an interactive dashboard prototype, think-aloud data, and interviews. Data were analyzed qualitatively through thematic analysis.

Results

Therapists use visualizations of upper limb information in the following ways: (1) to obtain objective data of patients’ activity levels, exercise, and neglect outside the clinic, (2) to engage patients in the rehabilitation process through education, motivation, and discussion of experiences with activities of daily living, and (3) to engage with other clinicians and researchers based on objective data. A major limitation is the lack of contextual data, which is needed by therapists to discern how movement data visualized on the dashboard relate to activities of daily living.

Conclusions

Upper limb information captured through wearable devices provides novel insights for therapists and helps to engage patients and other clinicians in therapy. Consideration needs to be given to the collection and visualization of contextual information to provide meaningful insights into patient engagement in activities of daily living. These findings open the door for further work to develop a fully functioning system and to trial it with patients and clinicians during therapy.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke

The ultimate goal of upper-limb rehabilitation after stroke is to promote real-world use, that is, use of the paretic upper-limb in everyday activities outside the clinic or laboratory. Although real-world use can be collected through self-report questionnaires, an objective indicator is preferred. Accelerometers are a promising tool. The current paper aims to explore the feasibility of accelerometers to measure upper-limb use after stroke and discuss the translation of this measurement tool into clinical practice. Accelerometers are non-invasive, wearable sensors that measure movement in arbitrary units called activity counts. Research to date indicates that activity counts are a reliable and valid index of upper-limb use. While most accelerometers are unable to distinguish between the type and quality of movements performed, recent advancements have used accelerometry data to produce clinically meaningful information for clinicians, patients, family and care givers. Despite this, widespread uptake in research and clinical environments remains limited. If uptake was enhanced, we could build a deeper understanding of how people with stroke use their arm in real-world environments. In order to facilitate greater uptake, however, there is a need for greater consistency in protocol development, accelerometer application and data interpretation.