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

A systematic review of instrumented assessments for upper limb function in cerebral palsy: current limitations and future directions

INTRODUCTION

Recently, interest in quantifying upper limb function in cerebral palsy has grown. However, the lack of reference tasks and protocols, have hindered the development of quantified movement analysis in clinical practice. This study aimed to evaluate existing instrumented assessments of upper limb function in cerebral palsy, with a focus on their clinical applicability, to identify reasons for the lack of adoption and provide recommendations for improving clinical relevance and utility.

METHODS

A systematic review was conducted by a multidisciplinary team of researchers and clinicians (Prospero CRD42023402382). PubMed and Web of Science databases were searched using relevant keywords and inclusion/exclusion criteria.

RESULTS

A total of 657 articles were initially identified, and after the selection process, 76 records were included for analysis comprising a total of 1293 patients with cerebral palsy. The quality assessment of the reviewed studies revealed a moderate overall quality, with deficiencies in sample size justification and participant information. Optoelectronic motion capture systems were predominantly used in the studies (N = 57/76). The population mainly consisted of individuals with spastic cerebral palsy (834/1293) with unilateral impairment (N = 1092/1293). Patients with severe functional impairment (MACS IV and V) were underrepresented with 3.4% of the 754 patients for whom the information was provided. Thirty-nine tasks were used across the articles. Most articles focused on unimanual activities (N = 66/76) and reach or reach and grasp (N = 51/76). Bimanual cooperative tasks only represented 3 tasks present in 4 articles. A total of 140 different parameters were identified across articles. Task duration was the most frequently used parameter and 23% of the parameters were used in only one article.

CONCLUSION

Further research is necessary before incorporating quantified motion analysis into clinical practice. Existing protocols focus on extensively studied populations and rely on costly equipment, limiting their practicality. Standardized unimanual tasks provide limited insights into everyday arm use. Balancing methodological requirements and performance evaluation flexibility is a challenge. Exploring the correlation between outcome parameters and therapeutic guidance could facilitate the integration of quantified movement assessment into treatment pathways.

Perspectives and expectations of stroke survivors using egocentric cameras for monitoring hand function at home: a mixed methods study

PURPOSE

Most stroke survivors have remaining upper limb impairment six months after stroke and require additional rehabilitation and help from family members to enhance their performance of daily activities. First-person (egocentric) video has been proposed to capture the activities of daily living (ADLs) of stroke survivors in order to assess their hand function at home. This study explored the experiences and expectations of stroke survivors regarding the use of egocentric cameras in daily life for rehabilitation applications.

METHODS

Twenty-one chronic stroke survivors recruited for the study were asked to record three sessions of 1.5 h of video of their ADLs at home over two weeks. Their experiences and expectations after completing the recordings were discussed using a structured questionnaire and a semi-structured interview. The questionnaire and interview data were analysed using descriptive statistics and content analysis, respectively. The results were further integrated using a mixed methods analysis for mutual explanation and elaboration.

RESULTS

The themes generated were Camera Usability, Privacy Concerns Related to Home Recordings, Future Use of the Camera in Public, and Information Usefulness. The participants perceived that the camera was easy to use, the information obtained from the recordings was beneficial, and no major concerns about recording at home. A discreet camera and a solution to privacy issues were prerequisites to recording tasks in public.

CONCLUSIONS

There was high acceptance among stroke survivors regarding the use of wearable cameras for rehabilitation purposes in the future. Concerns to be managed include discomfort, self-consciousness, and the privacy of others.Implications for rehabilitationThe egocentric camera was easy for the stroke survivors to use at home. However, they expressed a preference for cameras to be less noticeable and lighter in the future to minimize self-consciousness and discomfort.Expectations for future use of an egocentric camera for upper limb rehabilitation at home from the perspectives of stroke survivors included receiving feedback on their hand function in daily life and guidance on how to improve function.Privacy concerns of stroke survivors regarding recording activities of daily living were mostly avoidable by planning in advance. However, some personal hygiene tasks and virtual meetings were recorded by accident. A checklist of common activities that may raise privacy issues can be provided along with the camera to serve as a reminder to avoid these issues.

A deep learning wearable-based solution for continuous at-home monitoring of upper limb goal-directed movements

Introduction

Monitoring upper limb function is crucial for tracking progress, assessing treatment effectiveness, and identifying potential problems or complications. Hand goal-directed movements (GDMs) are a crucial aspect of daily life, reflecting planned motor commands with hand trajectories towards specific target locations. Previous studies have shown that GDM tasks can detect early changes in upper limb function in neurodegenerative diseases and can be used to track disease progression over time.

Methods

In this study, we used accelerometer data from stroke survivor participants and controls doing activities of daily living to develop an automated deep learning approach to detect GDMs. The model performance for detecting GDM or non-GDM from windowed data achieved an AUC of 0.9, accuracy 0.83, sensitivity 0.81, specificity 0.84 and F1 0.82.

Results

We further validated the utility of detecting GDM by extracting features from GDM periods and using these features to classify whether the measurements are collected from a stroke survivor or a control participant, and to predict the Fugl-Meyer assessment score from stroke survivors.

Discussion

This study presents a promising and reliable tool for monitoring upper limb function in a real-world setting, and assessing biomarkers related to upper limb health in neurological, neuromuscular and muscles disorders.

Somatotopic disruption of the functional connectivity of the primary sensorimotor cortex in complex regional pain syndrome type 1

In complex regional pain syndrome (CRPS), the representation area of the affected limb in the primary sensorimotor cortex (SM1) reacts abnormally during sensory stimulation and motor actions. We recorded 3T functional magnetic resonance imaging resting-state data from 17 upper-limb CRPS type 1 patients and 19 healthy control subjects to identify alterations of patients' SM1 function during spontaneous pain and to find out how the spatial distribution of these alterations were related to peripheral symptoms. Seed-based correlations and independent component analyses indicated that patients' upper-limb SM1 representation areas display (i) reduced interhemispheric connectivity, associated with the combined effect of intensity and spatial extent of limb pain, (ii) increased connectivity with the right anterior insula that positively correlated with the duration of CRPS, (iii) increased connectivity with periaqueductal gray matter, and (iv) disengagement from the other parts of the SM1 network. These findings, now reported for the first time in CRPS, parallel the alterations found in patients suffering from other chronic pain conditions or from limb denervation; they also agree with findings in healthy persons who are exposed to experimental pain or have used their limbs asymmetrically. Our results suggest that CRPS is associated with a sustained and somatotopically specific alteration of SM1 function, that has correspondence to the spatial distribution of the peripheral manifestations and to the duration of the syndrome.

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.

Validity of Novel Outcome Measures for Hand Function Performance After Stroke Using Egocentric Video

BACKGROUND

Evaluating upper limb (UL) interventions after stroke calls for outcome measures that describe impact on daily life in the community. UL use ratio has been used to quantify the performance domain of UL function, but generally focuses on arm use only. A hand use ratio could provide additional information about UL function after stroke. Additionally, a ratio based on the role of the more-affected hand in bilateral activities (stabilizer or manipulator) may also reflect hand function recovery. Egocentric video is a novel modality that can record both dynamic and static hand use and hand roles at home after stroke.

OBJECTIVE

To validate hand use and hand role ratios from egocentric video against standardized clinical UL assessments.

METHODS

Twenty-four stroke survivors recorded daily tasks in a home simulation laboratory and their daily routines at home using egocentric cameras. Spearman's correlation was used to compare the ratios with the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and Motor Activity Log-30 (MAL, Amount of Use (AoU), and Quality of Movement (QoM)).

RESULTS

Hand use ratio significantly correlated with the FMA-UE (0.60, 95% CI: 0.26, 0.81), ARAT (0.44, CI: 0.04, 0.72), MAL-AoU (0.80, CI: 0.59, 0.91), and MAL-QoM (0.79, CI: 0.57, 0.91). Hand role ratio had no significant correlations with the assessments.

CONCLUSION

Hand use ratio automatically extracted from egocentric video, but not hand role ratio, was found to be a valid measure of hand function performance in our sample. Further investigation is necessary to interpret hand role information.

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.

Indexes for motor performance assessment in job integration/reintegration of people with neuromuscular disorders: A systematic review

Individuals of working age affected by neuromuscular disorders frequently experience issues with their capacity to get employment, difficulty at work, and premature work interruption. Anyway, individuals with a disability could be able to return to work, thanks to targeted rehabilitation as well as ergonomic and training interventions. Biomechanical and physiological indexes are important for evaluating motor and muscle performance and determining the success of job integration initiatives. Therefore, it is necessary to determinate which indexes from the literature are the most appropriate to evaluate the effectiveness and efficiency of the return-to-work programs. To identify current and future valuable indexes, this study uses a systematic literature review methodology for selecting articles published from 2011 to March 30, 2021 from Scopus, Web of Science, and PubMed and for checking the eligibility and the potential bias risks. The most used indexes for motor performance assessment were identified, categorized, and analyzed. This review revealed a great potential for kinetic, kinematic, surface electromyography, postural, and other biomechanical and physiological indexes to be used for job integration/reintegration. Indeed, wearable miniaturized sensors, kinematic, kinetic, and sEMG-based indexes can be used to control collaborative robots, classify residual motor functions, and assess pre-post-rehabilitation and ergonomic therapies.

Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke

Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our study, 21 patients performed movement tasks of the Action Research Arm Test (ARAT), one of the most widely used clinical tests of upper limb motor functioning, while trained evaluators scored each task on pre-defined criteria. We collected data with just two wrist-worn inertial sensors to guarantee applicability across the continuum of care and used machine learning algorithms to estimate the ARAT task scores from sensor-derived features. Tasks scores were classified with approximately 80% accuracy. Linear regression between summed clinical task scores (across all tasks per patient) and estimates of sum task scores yielded a good fit (R 2 = 0.93; range reported in previous studies: 0.61-0.97). Estimates of the sum scores showed a mean absolute error of 2.9 points, 5.1% of the total score, which is smaller than the minimally detectable change and minimally clinically important difference of the ARAT when rated by a trained evaluator. We conclude that it is feasible to obtain accurate estimates of ARAT scores with just two wrist worn sensors. The approach enables administration of the ARAT in an objective, minimally supervised or remote fashion and provides the basis for a widespread use of wearable sensors in neurorehabilitation.

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.

Objective Assessment of Postoperative Morbidity After Breast Cancer Treatments with Wearable Activity Monitors: The "BRACELET" Study

Background

Current validated tools to measure upper limb dysfunction after breast cancer treatment, such as questionnaires, are prone to recall bias and do not enable comparisons between patients. This study aimed to test the feasibility of wearable activity monitors (WAMs) for achieving a continuous, objective assessment of functional recovery by measuring peri-operative physical activity (PA).

Methods

A prospective, single-center, non-randomized, observational study was conducted. Patients undergoing breast and axillary surgery were invited to wear WAMs on both wrists in the peri-operative period and then complete upper limb function (DASH) and quality-of-life (EQ-5D-5L) questionnaires. Statistical analyses were performed to determine the construct validity and concurrent validity of WAMs.

Results

The analysis included 39 patients with a mean age of 55 ± 13.2 years. Regain of function on the surgically treated side was observed to be an increase of arm activity as a percentage of preoperative levels, with the greatest increase observed between the postoperative days 1 and 2. The PA was significantly greater on the side not treated by surgery than on the surgically treated side after week 1 (mean PA, 75.8% vs. 62.3%; p < 0.0005) and week 2 (mean PA, 91.6% vs. 77.4%; p < 0.005). Subgroup analyses showed differences in recovery trends between different surgical procedures. Concurrent validity was demonstrated by a significant negative moderate correlation between the PA and DASH questionnaires (R = −0.506; p < 0.05).

Conclusion

This study demonstrated the feasibility and validity of WAMs to objectively measure postoperative recovery of upper limb function after breast surgery, providing a starting point for personalized rehabilitation through early detection of upper limb physical morbidity.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-021-10458-4.

Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

OBJECTIVE

Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis.

METHODS

Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two.

RESULTS

The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores.

CONCLUSION

This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data.

SIGNIFICANCE

This is important in the context of developing precision rehabilitation interventions.

Nursing effects of finger exercise on cognitive function and others for cerebral ischemic stroke patients

OBJECTIVE

To investigate the nursing effects of finger exercise training on cognitive function and others for patients with cerebral ischemic stroke (CIS).

METHODS

A total of 200 patients with CIS were selected in this prospective study. According to the random number table method, they were divided into control group (n=100, routine nursing) and research group (n=100, routine nursing combined with finger exercise training). Various scales were used to evaluate the cognitive function, hand function, upper limb motor function, wrist flexor muscle tone, degree of neurological impairment and ability of daily living (ADL) in the two groups before and after intervention. And the incidence of mild vascular cognitive impairment (VCI) after intervention was compared.

RESULTS

After intervention, Montreal Cognitive Assessment (MoCA), Mini Mental State Examination (MMSE), hand function, Fugl-meyer Assessment (FMA) and ADL scores in both groups were significantly increased, and those in the research group were significantly higher than those in the control group (all P<0.05). There were opposite trends in the Neurologic Functional Defect (NIHSS) and Modified Ashworth Scale (MAS) for wrist flexor scores (all P<0.05). The incidence of mild VCI in the research group was significantly lower than that in the control group (P<0.05).

CONCLUSION

On the basis of early rehabilitation nursing, combined finger exercise training can improve cognitive function, hand function, upper limb function and ADL for patients with CIS.

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.

Clip-On IMU System for Assessing Age-Related Changes in Hand Functions

Hand functions affect the instrumental activities of daily living. While functional outcome measures, such as a targeted box and block test, have been widely used in clinical settings and provide a useful measure of overall performance, the advent of a wearable Inertial Measurement Unit(IMU)-based system enables the examination of the specific performance and kinematic parameters of hand movements. This study proposed a novel clip-on IMU system to facilitate the clinically fitted measurements of fine-motor finger and wrist joint movements. Clinical validation was conducted with the aim of characterising age-related changes in hand functions, namely grasping, transporting, and releasing blocks. Eighteen young (age 20–31) and sixteen healthy older adults (age 75–89) were evaluated during the box and block test. The results demonstrated that an older age was characterized by slower movements and higher variations and kinematic alterations in the hand functions, such as a larger range of motions at the fingers as well as kinematic trajectories. The proposed IMU system and subsequent validations highlight the value of the performance and kinematics parameters for a more comprehensive understanding of fine-motor finger and wrist movements that could shed light on further implementations in clinical and practical settings.

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.

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients' responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.

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.

Feasibility of a set of wrist-worn novice devices for dual motion comparison of the upper limbs during lateral raise motions

Comparison of the upper limbs during natural kinematic motions is necessary for early detection of musculoskeletal imbalance between ipsilateral and contralateral sides in natural settings. Therefore, this study aims to evaluate the feasibility of a novice set of wrist-worn devices designed to assess and compare the dual kinematic motions of the upper limbs during lateral raises. The test-retest and the golden standard and novice device result comparisons were conducted for feasibility assessment of the novice set of devices. Pearson correlation coefficients between 0.65 and 0.88 (P<0.01) and effect sizes between 0.02 and 0.42 indicated feasible application of the novice devices. Considering correlation coefficient of 0.65 between the left and right upper limbs, the results show applicable feasibility of the novice device during lateral raises. In conclusion, the novice set of devices for comparing dual upper limb motions may be applied to assessing and comparing dual upper limb motions for limb motion comparisons and early detection of dysfunctional movements between the limbs.