Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter

A Large Harmonized Upper and Lower Limb Accelerometry Dataset: A Resource for Rehabilitation Scientists

Wearable sensors can measure movement in daily life, an outcome that is salient to patients, and have been critical to accelerating progress in rehabilitation research and practice. However, collecting and processing sensor data is burdensome, leaving many scientists with limited access to such data. To address these challenges, we present a harmonized, wearable sensor dataset that combines 2,885 recording days of sensor data from the upper and lower limbs from eight studies. The dataset includes 790 individuals ages 0 - 90, nearly equal sex proportions (53% male, 47% female), and representation from a range of demographic backgrounds (69.4% White, 24.9% Black, 1.8% Asian) and clinical conditions (46% neurotypical, 31% stroke, 7% Parkinson's disease, 6% orthopedic conditions, and others). The dataset is publicly available and accompanied by open source code and an app that allows for interaction with the data. This dataset will facilitate the use of sensor data to advance rehabilitation research and practice, improve the reproducibility and replicability of wearable sensor studies, and minimize costs and duplicated scientific efforts.

Quantifying the effects of sleep on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment

BACKGROUND

Despite the promise of wearable sensors for both rehabilitation research and clinical care, these technologies pose significant burden on data collectors and analysts. Investigations of factors that may influence the wearable sensor data processing pipeline are needed to support continued use of these technologies in rehabilitation research and integration into clinical care settings. The purpose of this study was to investigate the effect of one such factor, sleep, on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment and across a two-day wearing period.

METHODS

This was a secondary analysis of data collected during a prospective, longitudinal cohort study (n = 127 individuals, 62 with upper limb impairment and 65 without). Participants wore a wearable sensor on each wrist for 48 h. Five upper limb sensor variables were calculated over the full wear period (sleep included) and with sleep time removed (sleep excluded): preferred time, non-preferred time, use ratio, non-preferred magnitude and its standard deviation. Linear mixed effects regression was used to quantify the effect of sleep on each sensor variable and determine if the effect differed between people with and without upper limb impairment and across a two-day wearing period.

RESULTS

There were significant differences between sleep included and excluded for the variables preferred time (p < 0.001), non-preferred time (p < 0.001), and non-preferred magnitude standard deviation (p = 0.001). The effect of sleep was significantly different between people with and without upper limb impairment for one variable, non-preferred magnitude (p = 0.02). The effect of sleep was not substantially different across wearing days for any of the variables.

CONCLUSIONS

Overall, the effects of sleep on sensor-derived variables of upper limb accelerometry are small, similar between people with and without upper limb impairment and across a two-day wearing period, and can likely be ignored in most contexts. Ignoring the effect of sleep would simplify the data processing pipeline, facilitating the use of wearable sensors in both research and clinical practice.

Utility and usability of a wearable system and progressive-challenge cued exercise program for encouraging use of the more involved arm at-home after stroke-a feasibility study with case reports

BACKGROUND

Understanding the role of adherence to home exercise programs for survivors of stroke is critical to ensure patients perform prescribed exercises and maximize effectiveness of recovery.

METHODS

Survivors of hemiparetic stroke with impaired motor function were recruited into a 7-day study designed to test the utility and usability of a low-cost wearable system and progressive-challenge cued exercise program for encouraging graded-challenge exercise at-home. The wearable system comprised two wrist-worn MetaMotionR+ activity monitors and a custom smartphone app. The progressive-challenge cued exercise program included high-intensity activities (one repetition every 30 s) dosed at 1.5 h per day, embedded within 8 h of passive activity monitoring per day. Utility was assessed using measures of system uptime and cue response rate. Usability and user experience were assessed using well-validated quantitative surveys of system usability and user experience. Self-efficacy was assessed at the end of each day on a visual analog scale that ranged from 0 to 100.

RESULTS

The system and exercise program had objective utility: system uptime was 92 ± 6.9% of intended hours and the rate of successful cue delivery was 99 ± 2.7%. The system and program also were effective in motivating cued exercise: activity was detected within 5-s of the cue 98 ± 3.1% of the time. As shown via two case studies, accelerometry data can accurately reflect graded-challenge exercise instructions and reveal differentiable activity levels across exercise stages. User experience surveys indicated positive overall usability in the home settings, strong levels of personal motivation to use the system, and high degrees of satisfaction with the devices and provided training. Self-efficacy assessments indicated a strong perception of proficiency across participants (95 ± 5.0).

CONCLUSIONS

This study demonstrates that a low-cost wearable system providing frequent haptic cues to encourage graded-challenge exercise after stroke can have utility and can provide an overall positive user experience in home settings. The study also demonstrates how combining a graded exercise program with all-day activity monitoring can provide insight into the potential for wearable systems to assess adherence to-and effectiveness of-home-based exercise programs on an individualized basis.

Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning

AIM

To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP).

METHOD

Dual wrist-worn accelerometry data were collected from three females and seven males with unilateral CP (mean age = 10 years 2 months [SD 3 years]) while performing hand tasks during video-recorded play sessions. Video observers labelled instances of functional and non-functional hand use. Machine learning was compared to the conventional activity count approach for identifying unilateral hand movements as functional or non-functional. Correlation and agreement analyses compared the functional usage metrics derived from each method.

RESULTS

The best-performing machine learning approach had high precision and recall when trained on an individual basis (F1  = 0.896 [SD 0.043]). On an individual basis, the best-performing classifier showed a significant correlation (r = 0.990, p < 0.001) and strong agreement (bias = 0.57%, 95% confidence interval = -4.98 to 6.13) with video observations. When validated in a leave-one-subject-out scenario, performance decreased significantly (F1  = 0.584 [SD 0.076]). The activity count approach failed to detect significant differences in non-functional or functional hand activity and showed no significant correlation or agreement with the video observations.

INTERPRETATION

With further development, wearable accelerometry combined with machine learning may enable quantitative monitoring of everyday functional hand use in children with unilateral CP.

The feasibility of the adapted H-GRASP program for perceived and actual daily-life upper limb activity in the chronic phase post-stroke

(Purpose: Assessing feasibility and initial impact of the Home-Graded Repetitive Arm Supplementary Program combined with in-home accelerometer-based feedback (AH-GRASP) on perceived and actual daily-life upper limb (UL) activity in stroke survivors during the chronic phase with good UL motor function but low perceived daily-life activity. Material and methods: A 4-week intervention program (4 contact hours, 48 h self-practice) encompassing task-oriented training, behavioral techniques, phone-based support, monitoring, and weekly feedback sessions using wrist-worn accelerometery was implemented using a pre-post double baseline repeated measures design. Feasibility, clinical assessments, patient-reported outcomes, and accelerometer data were investigated. Results: Of the 34 individuals approached, nineteen were included (recruitment rate 56%). Two dropped out, one due to increased UL pain (retention rate 89%). Seven (41%) achieved the prescribed exercise target (120 min/day, six days/week). Positive patient experiences and improvements in UL capacity, self-efficacy, and contribution of the affected UL to overall activity (p < 0.05, small to large effect sizes) were observed. Additionally, seven participants (41%) surpassed the minimal clinically important difference in perceived UL activity.Conclusions: A home-based UL exercise program with accelerometer-based feedback holds promise for enhancing perceived and actual daily-life UL activity for our subgroup of chronic stroke survivors.

Optimizing Epoch Length and Activity Count Threshold Parameters in Accelerometry: Enhancing Upper Extremity Use Quantification in Cerebral Palsy

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP).

METHODS

A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150).

RESULTS

In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75).

CONCLUSIONS

The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use.

Grade-4/5 motor activity log-Brazil for post-stroke individuals with a severely impaired upper limb: a validity, reliability and measurement error study

PURPOSE

This study examined the clinimetrics of the Brazilian-Portuguese translation of the Grade-4/5 Motor Activity Log (MAL 4/5), which assesses everyday use of the more affected upper-limb (UL) in stroke survivors with moderate/severe or severe motor impairment.

MATERIALS AND METHODS

The translated MAL 4/5 was administered to 47 stroke survivors with moderate/severe or severe UL motor impairment. Accelerometers were worn on participants' wrists for five days on average prior to the first assessment. Test-retest and inter-rater reliabilities were assessed using the intraclass correlation coefficient (ICC), internal consistency using Cronbach's α, and construct validity was tested with correlations with the accelerometry. The measurement error (SEM) and the minimal detectable change (MDC) were calculated.

RESULTS

MAL4/5-Brazil's test-retest reliability (AOU: ICC = 0.84; QOU: ICC = 0.90), inter-rater reliability (AOU: ICC = 0.83; QOU: ICC = 0.91), internal consistency (Cronbach's α = 0.91 and 0.95 for AOU and QOU scales, respectively), the SEM and MDC were 0.3 and 0.8 points for the AOU subscale and 0.2 and 0.5 points for the QOU subscale, respectively. The construct validity (AOU scale: r = 0.67; QOU scale: r = 0.76) was high.

CONCLUSION

Grade-4/5 Motor Activity Log-Brazil is a reliable and valid instrument for assessing the more-affected UL use of stroke patients with moderate/severe or severe UL motor impairments.

Minimal Clinically Important Difference of Scales Reported in Stroke Trials: A Review

There is a growing awareness of the significance of using minimum clinically important differences (MCIDs) in stroke research. An MCID is the smallest change in an outcome measure that is considered clinically meaningful. This review is the first to provide a comprehensive summary of various scales and patient-reported outcome measures (PROMs) used in stroke research and their MCID values reported in the literature, including a concise overview of the concept of and methods for determining MCIDs in stroke research. Despite the controversies and limitations surrounding the estimation of MCIDs, their importance in modern clinical trials cannot be overstated. Anchor-based and distribution-based methods are recommended for estimating MCIDs, with patient self-evaluation being a crucial component in capturing the patient's perspective on their health. A combination of methods can provide a more comprehensive understanding of the clinical relevance of treatment effects, and incorporating the patient's perspective can enhance the care of stroke patients.

Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non-intrusive wearable sensors provides context for arm use and is essential for the development of a home rehabilitation system. However, the movement classification of stroke patients poses unique challenges, including variability and sparsity. To address these challenges, we collected movement data from 15 hemiparetic stroke patients (Stroke group) and 29 non-disabled individuals (ND group). The participants performed two different tasks, the range of motion (14 movements) task and the activities of daily living (56 movements) task, wearing five inertial measurement units in a home setting. We trained a 1D convolutional neural network and evaluated its performance for different training groups: ND-only, Stroke-only, and ND and Stroke jointly. We further compared the model performance with data augmentation from axis rotation and investigated how the performance varied based on the asymmetry of movements. The joint training of ND + Stroke yielded an increased F1-score by a margin of 31.6% and 10.6% compared to ND-only training and Stroke-only training, respectively. Data augmentation further enhanced F1-scores across all conditions by an average of 11.3%. Finally, asymmetric movements decreased the F1-score by 25.9% compared to symmetric movements in the Stroke group, indicating the importance of asymmetry in movement classification.

Upper extremity asymmetry due to nerve injuries or central neurologic conditions: a scoping review

BACKGROUND

Peripheral nerve injuries and central neurologic conditions can result in extensive disabilities. In cases with unilateral impairment, assessing the asymmetry between the upper extremity has been used to assess outcomes of treatment and severity of injury. A wide variety of validated and novel tests and sensors have been utilized to determine the upper extremity asymmetry. The purpose of this article is to review the literature and define the current state of the art for describing upper extremity asymmetry in patients with peripheral nerve injuries or central neurologic conditions.

METHOD

An electronic literature search of PubMed, Scopus, Web of Science, OVID was performed for publications between 2000 to 2022. Eligibility criteria were subjects with neurological conditions/injuries who were analyzed for dissimilarities in use between the upper extremities. Data related to study population, target condition/injury, types of tests performed, sensors used, real-world data collection, outcome measures of interest, and results of the study were extracted. Sackett's Level of Evidence was used to judge the quality of the articles.

RESULTS

Of the 7281 unique articles, 112 articles met the inclusion criteria for the review. Eight target conditions/injuries were identified (Brachial Plexus Injury, Cerebral Palsy, Multiple Sclerosis, Parkinson's Disease, Peripheral Nerve Injury, Spinal Cord Injury, Schizophrenia, and stroke). The tests performed were classified into thirteen categories based on the nature of the test and data collected. The general results related to upper extremity asymmetry were listed for all the reviewed articles. Stroke was the most studied condition, followed by cerebral palsy, with kinematics and strength measurement tests being the most frequently used tests. Studies with a level of evidence level II and III increased between 2000 and 2021. The use of real-world evidence-based data, and objective data collection tests also increased in the same period.

CONCLUSION

Adequately powered randomized controlled trials should be used to study upper extremity asymmetry. Neurological conditions other than stroke should be studied further. Upper extremity asymmetry should be measured using objective outcome measures like motion tracking and activity monitoring in the patient's daily living environment.

Understanding stroke survivors' preferences regarding wearable sensor feedback on functional movement: a mixed-methods study

BACKGROUND

In stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors' preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine the ease of understanding and movement encouragement of feedback based on wearable sensor data (both arm/hand use and mobility) for stroke survivors and to identify preferences for feedback metrics (mode, content, frequency, and timing).

METHODS

A sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of feedback about arm/hand use, step counts and gait symmetry. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change.

RESULTS

Arm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: (1) Motivation for behavior change, (2) Real-time feedback based on individual goals, and (3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting.

CONCLUSION

The resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment.

Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment

(1) Background: Being able to objectively assess upper limb (UL) dysfunction in breast cancer survivors (BCS) is an emerging issue. This study aims to determine the accuracy of a pre-trained lab-based machine learning model (MLM) to distinguish functional from non-functional arm movements in a home situation in BCS. (2) Methods: Participants performed four daily life activities while wearing two wrist accelerometers and being video recorded. To define UL functioning, video data were annotated and accelerometer data were analyzed using a counts threshold method and an MLM. Prediction accuracy, recall, sensitivity, f1-score, 'total minutes functional activity' and 'percentage functionally active' were considered. (3) Results: Despite a good MLM accuracy (0.77-0.90), recall, and specificity, the f1-score was poor. An overestimation of the 'total minutes functional activity' and 'percentage functionally active' was found by the MLM. Between the video-annotated data and the functional activity determined by the MLM, the mean differences were 0.14% and 0.10% for the left and right side, respectively. For the video-annotated data versus the counts threshold method, the mean differences were 0.27% and 0.24%, respectively. (4) Conclusions: An MLM is a better alternative than the counts threshold method for distinguishing functional from non-functional arm movements. However, the abovementioned wrist accelerometer-based assessment methods overestimate UL functional activity.

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.

Outcomes Associated with a Single Joystick-Operated Ride-on-Toy Navigation Training Incorporated into a Constraint-Induced Movement Therapy Program: A Pilot Feasibility Study

Our research aims to evaluate the utility of joystick-operated ride-on-toys (ROTs) as therapeutic adjuncts to improve upper extremity (UE) function in children with hemiplegic cerebral palsy (HCP). This study assessed changes in affected UE use and function following a three-week ROT navigation training incorporated into an existing constraint-induced movement therapy (CIMT) camp in 11 children (3-14 years old) with HCP. We report changes in scores on the standardized Shriners Hospital Upper Extremity Evaluation (SHUEE) from pretest-to-posttest and changes from early-to-late sessions in percent time spent by the affected arm in: (a) "moderate-to-vigorous activity", "light activity" and "no activity" bouts based on accelerometer data and (b) "independent", "assisted", and "no activity" bouts based on video data. We also explored relationships between standardized measures and training-specific measures of affected UE activity. We found small-to-medium improvements in the SHUEE scores. Between 90 and 100% of children also showed medium-to-large improvements in affected UE activity from early-to-late sessions using accelerometers and small improvements via video-based assessments. Exploratory analyses suggested trends for relationships between pretest-posttest and training-specific objective and subjective measures of arm use and function. Our pilot data suggest that single joystick-operated ROTs may serve as motivating, child-friendly tools that can augment conventional therapies such as CIMT to boost treatment dosing, promote affected UE movement practice during real-world navigation tasks, and ultimately improve functional outcomes in children with HCP.

Comparison of wrist actimetry variables of paretic upper limb use in post stroke patients for ecological monitoring

BACKGROUND

To date, many wrist actimetric variables dedicated to measuring the upper limbs (UL) in post-stroke patients have been developed but very few comparisons have been made between them. The objective of this study was to compare different actimetric variables of the ULs between a stroke and healthy population.

METHODS

Accelerometers were worn continuously for a period of 7 days on both wrists of 19 post-stroke hemiparetic patients as well as 11 healthy subjects. Various wrist actimetry variables were calculated, including the Jerk ratio 50 (JR50, cumulative probability that the Jerk Ratio is between 1 and 2), absolute (FuncUse30) and relative (FuncUseRatio30) amounts of functional use of movements of the ULs with angular amplitude greater than 30°, and absolute (UH) and relative (UseHoursRatio) use hours.

RESULTS

FuncUse30, FuncUseRatio30, UH, UseHoursRatio and JR50 of the paretic UL of stroke patients were significantly lower than in the non-dominant UL of healthy subjects. Comparing the ratio variables in stroke patients, FuncUseRatio30 was significantly lower than UseHoursRatio and JR50, suggesting a more clinically sensitive variable to monitor. In an exploratory analysis, FuncUseRatio tends to decrease with angular range of motion for stroke patients while it remains stable and close to 1 for healthy subjects. UseHoursRatio, FuncUseRatio30 and JR50 show linear correlation with Fugl-Meyer score (FM), with r² equal to 0.53, 0.35 and 0.21, respectively.

CONCLUSION

This study determined that the FuncUseRatio30 variable provides the most sensitive clinical biomarker of paretic UL use in post-stroke patients, and that FuncUseHours-angular range of motion relationship allows the identification of the UL behaviour of each patient. This ecological information on the level of functional use of the paretic UL can be used to improve follow-up and develop patient-specific therapy.

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3-85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4-72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

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.

The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review

PURPOSE

To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment.

MATERIALS AND METHODS

The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis.

RESULTS

We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE.

CONCLUSIONS

Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.IMPLICATIONS FOR REHABILITATIONSensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.

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.

Individuals with Higher Levels of Physical Activity after Stroke Show Comparable Patterns of Myelin to Healthy Older Adults

Background

Myelin asymmetry ratios (MARs) relate and contribute to motor impairment and function after stroke. Physical activity (PA) may induce myelin plasticity, potentially mitigating hemispheric myelin asymmetries that can occur after a stroke.

Objective

The aim of this study was to determine whether individuals with higher levels of PA showed lower MAR compared to individuals with lower levels of PA.

Methods

Myelin water fraction was obtained from 5 bilateral motor regions in 22 individuals with chronic stroke and 26 healthy older adults. Activity levels were quantified with wrist accelerometers worn for a period of 72 hours (3 days). Higher and lower PA levels were defined by a cluster analysis within each group.

Results

MAR was similar regardless of PA level within the older adult group. Compared to the higher PA stroke group, lower PA stroke participants displayed greater MAR. There was no difference in MAR between the stroke and older adult higher PA groups. Within the lower PA groups, individuals with stroke showed greater MAR compared to the older adults. Arm impairment, lesion volume, age, time since stroke, and preferential arm use were not different between the PA stroke groups, suggesting that motor impairment severity and extent of brain damage did not drive differences in PA.

Conclusion

Individuals who have had a stroke and are also physically active display lower MAR (i.e., similar myelin in both hemispheres) in motor regions. High levels of PA may be neuroprotective and mitigate myelin asymmetries once a neurological insult, such as a stroke, occurs. Alternately, it is possible that promoting high levels of PA after a stroke may reduce myelin asymmetries.

Determinants of Different Aspects of Upper-Limb Activity after Stroke

We examined factors associated with different aspects of upper-limb (UL) activity in chronic stroke to better understand and improve UL activity in daily life. Three different aspects of UL activity were represented by four sensor measures: (1) contribution to activity according to activity ratio and magnitude ratio, (2) intensity of activity according to bilateral magnitude, and (3) variability of activity according to variation ratio. We combined data from a Belgian and Danish patient cohort (n = 126) and developed four models to determine associated factors for each sensor measure. Results from standard multiple regression show that motor impairment (Fugl−Meyer assessment) accounted for the largest part of the explained variance in all sensor measures (18−61%), with less motor impairment resulting in higher UL activity values (p < 0.001). Higher activity ratio, magnitude ratio, and variation ratio were further explained by having the dominant hand affected (p < 0.007). Bilateral magnitude had the lowest explained variance (adjusted R2 = 0.376), and higher values were further associated with being young and female. As motor impairment and biological aspects accounted for only one- to two-thirds of the variance in UL activity, rehabilitation including behavioral strategies might be important to increase the different aspects of UL activity.

Relationship Between Body-Specific Attention to a Paretic Limb and Real-World Arm Use in Stroke Patients: A Longitudinal Study

Learned nonuse is a major problem in upper limb (UL) rehabilitation after stroke. Among the various factors that contribute to learned nonuse, recent studies have focused on body representation of the paretic limb in the brain. We previously developed a method to measure body-specific attention, as a marker of body representation of the paretic limb and revealed a decline in body-specific attention to the paretic limb in chronic stroke patients by a cross-sectional study. However, longitudinal changes in body-specific attention and paretic arm use in daily life (real-world arm use) from the onset to the chronic phase, and their relationship, remain unknown. Here, in a longitudinal, prospective, observational study, we sought to elucidate the longitudinal changes in body-specific attention to the paretic limb and real-world arm use, and their relationship, by using accelerometers and psychophysical methods, respectively, in 25 patients with subacute stroke. Measurements were taken at baseline (TBL), 2 weeks (T2w), 1 month (T1M), 2 months (T2M), and 6 months (T6M) after enrollment. UL function was measured using the Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT). Real-world arm use was measured using accelerometers on both wrists. Body-specific attention was measured using a visual detection task. The UL function and real-world arm use improved up to T6M. Longitudinal changes in body-specific attention were most remarkable at T1M. Changes in body-specific attention up to T1M correlated positively with changes in real-world arm use up to T6M, and from T1M to T6M, and the latter more strongly correlated with changes in real-world arm use. Changes in real-world arm use up to T2M correlated positively with changes in FMA up to T2M and T6M. No correlation was found between body-specific attention and FMA scores. Thus, these results suggest that improved body-specific attention to the paretic limb during the early phase contributes to increasing long-term real-world arm use and that increased real-world use is associated with the recovery of UL function. Our results may contribute to the development of rehabilitation strategies to enhance adaptive changes in body representation in the brain and increase real-world arm use after stroke.

Upper Limb Performance in Daily Life Approaches Plateau Around Three to Six Weeks Post-stroke

Background. Wearable sensors allow for direct measurement of upper limb (UL) performance in daily life. Objective. To map the trajectory of UL performance and its relationships to other factors post-stroke. Methods. Participants (n = 67) with first stroke and UL paresis were assessed at 2, 4, 6, 8, 12, 16, 20, and 24 weeks after stroke. Assessments captured UL impairment (Fugl-Meyer), capacity for activity (Action Research Arm Test), and performance of activity in daily life (accelerometer variables of use ratio and hours of paretic limb activity), along with other potential modifying factors. We modeled individual trajectories of change for each measurement level and the moderating effects on UL performance trajectories. Results. Individual trajectories were best fit with a 3-parameter logistic model, capturing the rapid growth early after stroke within the longer data collection period. Plateaus (90% of asymptote) in impairment (bootstrap mean ± SE: 32 ± 4 days post-stroke) preceded those in capacity (41 ± 4 days). Plateau in performance, as measured by the use ratio (24 ± 5 days), tended to precede plateaus in impairment and capacity. Plateau in performance, as measured by hours of paretic activity (41 ± 6 days), occurred at a similar time to that of capacity and slightly lagged impairment. Modifiers of performance trajectories were capacity, concordance, UL rehabilitation, depressive symptomatology, and cognition. Conclusions. Upper limb performance in daily life approached plateau 3 to 6 weeks post-stroke. Individuals with stroke started to achieve a stable pattern of UL use in daily life early, often before neurological impairments and functional capacity started to stabilize.

Daily Life Upper Limb Activity for Patients with Match and Mismatch between Observed Function and Perceived Activity in the Chronic Phase Post Stroke

We investigated actual daily life upper limb (UL) activity in relation to observed UL motor function and perceived UL activity in chronic stroke in order to better understand and improve UL activity in daily life. In 60 patients, we collected (1) observed UL motor function (Fugl-Meyer Assessment (FMA-UE)), (2) perceived UL activity (hand subscale of the Stroke Impact Scale (SIS-Hand)), and (3) daily life UL activity (bilateral wrist-worn accelerometers for 72 h) data. Data were compared between two groups of interest, namely (1) good observed (FMA-UE >50) function and good perceived (SIS-Hand >75) activity (good match, n = 16) and (2) good observed function but low perceived (SIS-Hand ≤75) activity (mismatch, n = 15) with Mann-Whitney U analysis. The mismatch group only differed from the good match group in perceived UL activity (median (Q1-Q3) = 50 (30-70) versus 93 (85-100); p < 0.001). Despite similar observed UL motor function and other clinical characteristics, the affected UL in the mismatch group was less active in daily life compared to the good match group (p = 0.013), and the contribution of the affected UL compared to the unaffected UL for each second of activity (magnitude ratio) was lower (p = 0.022). We conclude that people with chronic stroke with low perceived UL activity indeed tend to use their affected UL less in daily life despite good observed UL motor function.

mHealth technologies used to capture walking and arm use behavior in adult stroke survivors: a scoping review beyond measurement properties

PURPOSE

We aimed to provide a critical review of measurement properties of mHealth technologies used for stroke survivors to measure the amount and intensity of functional skills, and to identify facilitators and barriers toward adoption in research and clinical practice.

MATERIALS AND METHODS

Using Arksey and O'Malley's framework, two independent reviewers determined eligibility and performed data extraction. We conducted an online consultation survey exercise with 37 experts.

RESULTS

Sixty-four out of 1380 studies were included. A majority reported on lower limb behavior (n = 32), primarily step count (n = 21). Seventeen studies reported on arm-hand behaviors. Twenty-two studies reported metrics of intensity, 10 reported on energy expenditure. Reliability and validity were the most frequently reported properties, both for commercial and non-commercial devices. Facilitators and barriers included: resource costs, technical aspects, perceived usability, and ecological legitimacy. Two additional categories emerged from the survey: safety and knowledge, attitude, and clinical skill.

CONCLUSIONS

This provides an initial foundation for a field experiencing rapid growth, new opportunities and the promise that mHealth technologies affords for envisioning a better future for stroke survivors. We synthesized findings into a set of recommendations for clinicians and clinician-scientists about how best to choose mHealth technologies for one's individual objective.Implications for RehabilitationRehabilitation professionals are encouraged to consider the measurement properties of those technologies that are used to monitor functional locomotor and object-interaction skills in the stroke survivors they serve.Multi-modal knowledge translation strategies (research synthesis, educational courses or videos, mentorship from experts, etc.) are available to rehabilitation professionals to improve knowledge, attitude, and skills pertaining to mHealth technologies.Consider the selection of commercially available devices that are proven to be valid, reliable, accurate, and responsive to the targeted clinical population.Consider usability and privacy, confidentiality and safety when choosing a specific device or smartphone 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.

Accelerometry as an objective measure of upper-extremity activity

Most studies evaluating the effectiveness of treatments targeting shoulder pathologies use subjective outcome measures such as self-administered questionnaires. To date, there are no validated tools that objectively measure shoulder-specific functional activity. The purpose of this study was to validate wearable accelerometers as an objective proxy for shoulder activity. Ten healthy volunteers wore accelerometers placed at both wrists, the dominant upper arm and the chest while performing standardised shoulder and non-shoulder activities. Recorded tridimensional acceleration was computed into activity counts for epochs of 10 s. Receiver operating characteristics (ROC) curves were built to determine the optimal configuration to classify shoulder-type activities. For single accelerometer placement, the area under the ROC curve (AUC) was optimal for the 10-s epoch (AUC = 0.779) using the wrist placement, with a sensitivity of 94.1% and specificity of 67.5%. The combined upper arm and chest placement had an AUC of 0.985 (94.8% sensitivity, 94.8% specificity). Dual-accelerometer placement (upper arm and chest) is the optimal configuration to classify shoulder activity. However, a sole wrist-based accelerometer can be used as an objective proxy for shoulder activity in long-term unsupervised monitoring with excellent sensitivity and acceptable specificity.

Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.

Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.

Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.

Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.

Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

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.

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Background

Standardized assessments are used in rehabilitation clinics after stroke to measure restoration versus compensatory movements of the upper limb. Accelerometry is an emerging tool that can bridge the gap between in- and out-of-clinic assessments of the upper limb, but is limited in that it currently does not capture the quality of a person’s movement, an important concept to assess compensation versus restoration. The purpose of this analysis was to characterize how accelerometer variables may reflect upper limb compensatory movement patterns after stroke.

Methods

This study was a secondary analysis of an existing data set from a Phase II, single-blind, randomized, parallel dose–response trial (NCT0114369). Sources of data utilized were: (1) a compensatory movement score derived from video analysis of the Action Research Arm Test (ARAT), and (2) calculated accelerometer variables quantifying time, magnitude and variability of upper limb movement from the same time point during study participation for both in-clinic and out-of-clinic recording periods.

Results

Participants had chronic upper limb paresis of mild to moderate severity. Compensatory movement scores varied across the sample, with a mean of 73.7 ± 33.6 and range from 11.5 to 188. Moderate correlations were observed between the compensatory movement score and each accelerometer variable. Accelerometer variables measured out-of-clinic had stronger relationships with compensatory movements, compared with accelerometer variables in-clinic. Variables quantifying time, magnitude, and variability of upper limb movement out-of-clinic had relationships to the compensatory movement score.

Conclusions

Accelerometry is a tool that, while measuring movement quantity, can also reflect the use of general compensatory movement patterns of the upper limb in persons with chronic stroke. Individuals who move their limbs more in daily life with respect to time and variability tend to move with less movement compensations and more typical movement patterns. Likewise, individuals who move their paretic limbs less and their non-paretic limb more in daily life tend to move with more movement compensations at all joints in the paretic limb and less typical movement patterns.

Implementation of Wearable Sensing Technology for Movement: Pushing Forward into the Routine Physical Rehabilitation Care Field

While the promise of wearable sensor technology to transform physical rehabilitation has been around for a number of years, the reality is that wearable sensor technology for the measurement of human movement has remained largely confined to rehabilitation research labs with limited ventures into clinical practice. The purposes of this paper are to: (1) discuss the major barriers in clinical practice and available wearable sensing technology; (2) propose benchmarks for wearable device systems that would make it feasible to implement them in clinical practice across the world and (3) evaluate a current wearable device system against the benchmarks as an example. If we can overcome the barriers and achieve the benchmarks collectively, the field of rehabilitation will move forward towards better movement interventions that produce improved function not just in the clinic or lab, but out in peoples' homes and communities.

Protocol for a Randomized Controlled Trial of CI Therapy for Rehabilitation of Upper Extremity Motor Deficit: The Bringing Rehabilitation to American Veterans Everywhere Project

Constraint-induced movement therapy (CI therapy) has been shown to reduce disability for individuals with upper extremity (UE) hemiparesis following different neurologic injuries. This article describes the study design and methodological considerations of the Bringing Rehabilitation to American Veterans Everywhere (BRAVE) Project, a randomized controlled trial of CI therapy to improve the motor deficit of participants with chronic and subacute traumatic brain injury. Our CI therapy protocol comprises 4 major components: (1) intensive training of the more-affected UE for target of 3 hour/day for 10 consecutive weekdays, (2) a behavioral technique termed shaping during training, (3) a "transfer package," 0.5 hour/day, of behavioral techniques to transfer therapeutic gains from the treatment setting to the life situation, and (4) prolonged restraint of use of the UE not being trained. The primary endpoint is posttreatment change on the Motor Activity Log, which assesses the use of the more-affected arm outside the laboratory in everyday life situations. Data from a number of secondary outcome measures are also being collected and can be categorized as physical, genomic, biologic, fitness, cognitive/behavioral, quality of life, and neuroimaging measures.

Accelerometer Measurements Indicate That Arm Movements of Children With Cerebral Palsy Do Not Increase After Constraint-Induced Movement Therapy (CIMT)

IMPORTANCE

Constraint-induced movement therapy (CIMT) is a common treatment for children with unilateral cerebral palsy (CP). Although clinic-based assessments have demonstrated improvements in arm function after CIMT, whether these changes are translated and sustained outside of a clinic setting remains unclear.

OBJECTIVE

Accelerometers were used to quantify arm movement for children with CP 1 wk before, during, and 4 wk or more after CIMT; measurements were compared with those from typically developing (TD) peers.

DESIGN

Observational.

SETTING

Tertiary hospital and community.

PARTICIPANTS

Seven children with CP (5 boys, 2 girls; average [AVE] age ± standard deviation [SD] = 7.4 ± 1.2 yr) and 7 TD peers (2 boys, 5 girls; AVE age ± SD = 7.0 ± 2.3 yr).

INTERVENTION

30-hr CIMT protocol.

OUTCOMES AND MEASURES

Use ratio, magnitude ratio, and bilateral magnitude were calculated from the accelerometer data. Clinical measures were administered before and after CIMT, and parent surveys assessed parent and child perceptions of wearing accelerometers.

RESULTS

During CIMT, the frequency and magnitude of paretic arm use among children with CP increased in the clinic and in daily life. After CIMT, although clinical scores showed sustained improvement, the children's accelerometry data reverted to baseline values. Children and parents in both cohorts had positive perceptions of accelerometer use.

CONCLUSIONS AND RELEVANCE

The lack of sustained improvement in accelerometry metrics after CIMT suggests that therapy gains did not translate to increased movement outside the clinic. Additional therapy may be needed to help transfer gains outside the clinic.

WHAT THIS ARTICLE ADDS

Accelerometer measurements were effective at monitoring arm movement outside of the clinic during CIMT and suggested that additional interventions may be needed after CIMT to sustain benefits.

Characterizing upper extremity motor behavior in the first week after stroke

Background

Animal models of brain recovery identify the first days after lesioning as a time of great flux in sensorimotor function and physiology. After rodent motor system lesioning, daily skill training in the less affected forelimb reduces skill acquisition in the more affected forelimb. We asked whether spontaneous human motor behaviors of the less affected upper extremity (UE) early after stroke resemble the animal training model, with the potential to suppress clinical recovery.

Methods

This prospective observational study used a convenience sample of patients (n = 25, mean 4.5 ±1.8) days after stroke with a wide severity range; Controls were hospitalized for non-neurological conditions (n = 12). Outcome measures were Accelerometry, Upper-Extremity Fugl-Meyer (UEFM), Action Research Arm Test (ARAT), Shoulder Abduction/ Finger Extension Test (SAFE), NIH Stroke Scale (NIHSS).

Results

Accelerometry indicated total paretic UE movement was reduced compared to controls, primarily due to a 44% reduction of bilateral UE use. Unilateral paretic movement was unchanged. Thus, movement shifted early after stroke; bilateral use was reduced and unilateral use of the non-paretic UE was increased by 77%. Low correlations between movement time and motor performance prompted an exploratory factor analysis (EFA) revealing a 2-component solution; motor performance tests load on one component (motor performance) whereas accelerometry-derived variables load on a second orthogonal component (quantity of movement).

Conclusions

Early after stroke, spontaneous overall UE movement is reduced, and movement shifts to unilateral use of the non-paretic UE. Two mechanisms that could influence motor recovery may already be in place 4.5 ± 1.8 days post stroke: (1) the overuse of the less affected UE, which could set the stage for learned non-use and (2) skill acquisition in the non-paretic limb that could impede recovery. Accurate UE motor assessment requires two independent constructs: motor performance and quantity of movement. These findings provide opportunities and measurement methods for studies to develop new behaviorally-based stroke recovery treatments that begin early after onset.

Does the addition of concurrent visual feedback increase adherence to a home exercise program in people with stroke: a single-case series?

OBJECTIVE

Evidence is accumulating for the potential benefits of technology use in stroke rehabilitation. However, few studies have examined ways in which technology can be used to increase adherence to programs after discharge from rehabilitation. The aim of this study was to determine if the addition of concurrent visual feedback, via a tablet computer, increased adherence to an exercise program following stroke. Ten participants were provided with a self-administered exercise program and were asked to perform 60 min of the exercises daily. After a baseline phase (1 week), participants were given a tablet computer (2 weeks) and were asked to video record each exercise session. The tablet computer was removed during the fourth week of the program.

RESULTS

Exercise duration, measured via wrist-worn accelerometry, was investigated over the 4 weeks using the two-standard deviation (2 SD) band method. A statistically significant effect was observed in four out of ten cases, demonstrated by two successive data points occurring outside the 2 SD band during the intervention phase, suggesting that adherence was increased in response to the tablet computer use. This preliminary study indicates that the use of visual feedback, via a tablet computer, may increase adherence to an exercise program in people with stroke. Trial registration ACTRN: ACTRN12620000252910 (26 February 2020, Retrospectively registered).

Feasibility of using acceleration-derived jerk to quantify bimanual arm use

BACKGROUND

Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts" - a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury.

METHOD

We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (non-paretic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics.

RESULTS

The JR50 differentiated between the CP and TD cohorts (CP = 0.578 ± 0.041 before CIMT, TD = 0.506 ± 0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50 = 0.378 ± 0.125 during CIMT, 0.591 ± 0.057 after CIMT). The JR was strongly correlated with UR and MR (r = - 0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945 ± 0.015.

CONCLUSIONS

Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.

Patient-Generated Actigraphy Data as a Novel Outcomes Instrument in Carpal Tunnel Syndrome

BACKGROUND

Currently, we lack objective measures to quantify outcomes in carpal tunnel syndrome. Instead, surgeons rely on patient-reported outcomes measures (PROMs) to assess the effect of carpal tunnel release (CTR). We assessed the validity and reliability of wearable activity monitors to objectively characterize the functional and sleep impact of CTR. We hypothesized that actigraphy could detect changes in sleep and activity and would demonstrate short-term impairment due the operative procedure.

METHODS

This pilot, prospective, cohort study compared validated PROMS with actigraphy data obtained via wearable activity monitors (ActiGraph Link; ActiGraph Corp, Pensacola, Fla). Subjects completed baseline questionnaires and wore their device for 1 week preoperatively as a baseline. Subjects then underwent open CTR, wearing actigraphy devices for 4 weeks and completing questionnaires at 2 and 4 weeks postoperatively. Preintervention and postintervention data were compared using paired-sample t test. The Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire data were analyzed in accordance with published PROMIS scoring manuals and raw scores were converted to standardized T scores.

RESULTS

Twenty subjects (5 males, 15 females) with moderate or severe carpal tunnel syndrome were enrolled. The mean age was 57.7 years. The PROMIS 29 average cumulative T score was 42.9 with average change of -0.072 preoperatively versus postoperatively. Average sleep disturbance T score was 38.9 preoperatively and 41.4 postoperatively. There was no statistically significant difference in T score in any domain. The mean actigraphy activity data demonstrated near immediate return to baseline activity. Actigraphy sleep data demonstrate improvement in sleep fragmentation and decreased duration of awakenings.

CONCLUSIONS

Overall, patient-generated data detected differences in sleep and activity preoperatively versus postoperatively and demonstrated only a short period of activity disruption after CTR, which may be used when counseling patients. These data support actigraphy as a viable adjunct to traditional PROMS to evaluate the impact of surgical intervention and therefore may be useful in the study of other diseases affecting the upper extremity.

Using accelerometry for measurement of motor behavior in children: Relationship of real-world movement to standardized evaluation

BACKGROUND

When detected, children with asymmetrical motor impairment are referred for therapeutic interventions to maximize the child's ability to reach their health and developmental potential. Referal is dependent on standardized evaluation, which rarely examines upper extremity (UE) function within the context of real-world activity. Accelerometry provides an efficient method to objectively measure movement in children. The purpose of this study was to compare accelerometry to clinical assessment, specifically the Melbourne Assessment of Unilateral Upper Limb Function-2 (MA-2).

METHODS

A total of 52 children between 1-17 years of age with asymmetrical motor deficits and age matched controls participated in this study. Participants wore bilateral accelerometers for 4 x 25 h. The use ratio (UR) and mono-arm use index (MAUI) were calculated to quantify asymmetrical impairment. The Melbourne Assessment of Unilateral Upper Limb Function-2 (MA-2) was administered and compared to accelerometry variables.

RESULTS

The UR and MAUI were significantly different in children with and without deficits. The MAUI was significantly correlated with all domains of the MA-2: accuracy (r = 0.44, p = 0.026); fluency (r = 0.52, p = 0.006); dexterity (r = 0.53, p = 0.005); and range of motion (r = 0.49, p = 0.011).

CONCLUSIONS

Our findings suggest a relationship between real-world movement and clinical evaluation.

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.

Upper Limb Performance in Daily Life Improves Over the First 12 Weeks Poststroke

Background. Upper limb (UL) performance, or use, in daily life is complex and likely influenced by many factors. While the recovery trajectory of UL impairment poststroke is well documented, little is known about the recovery trajectory of sensor-measured UL performance in daily life early after stroke and the potential moderating role of psychosocial factors. Objective. To examine the recovery trajectory of UL performance within the first 12 weeks poststroke and characterize the potential moderating role of belief, confidence, and motivation on UL performance. Methods. This was a longitudinal, prospective cohort study quantifying UL performance and related psychosocial factors early after stroke. UL performance was quantified via bilateral, wrist-worn accelerometers over 5 assessment sessions for 24 hours. Belief, confidence, and motivation to use the paretic UL, and self-perceived barriers to UL recovery were quantified via survey. Change in 4 accelerometer variables and the moderating role of psychosocial factors was tested using hierarchical linear modeling. The relationship between self-perceived barriers and UL performance was tested via Spearman rank-order correlation analysis. Results. UL performance improved over the first 12 weeks after stroke. Belief, confidence, and motivation did not moderate UL performance over time. There was a negative relationship between UL performance and self-perceived barriers to UL recovery at week 2, which declined over time. Conclusions. Sensor-measured UL performance can improve early after stroke. Early after stroke, rehabilitation interventions may not need to directly target belief, confidence, and motivation but may instead focus on reducing self-perceived barriers to UL recovery.

Measuring Reliability of Movement With Accelerometry: Fitbit® Versus ActiGraph®

OBJECTIVE

We sought to determine the reliability of the Fitbit^® Flex™ compared with the ActiGraph^® Bluetooth^® Smart wGT3X-BT wireless activity monitor and to contribute to the clinical utility of accelerometry measurement of upper extremity (UE) movement.

METHOD

Two studies were conducted at different sites with healthy adult participants. In Study 1, participants wore both accelerometers on both wrists during everyday activities for a 24-hr period. In Study 2, participants wore both accelerometers on the dominant wrist for 4 hr during an active period of the day. All participants wore the accelerometers during regular daily activities.

RESULTS

Data recorded from the Fitbit and the ActiGraph showed a high positive correlation; however, the Fitbit recorded significantly fewer movements than the ActiGraph.

CONCLUSION

Although the Fitbit and the ActiGraph measure UE activity similarly, the Fitbit was not as sensitive as the ActiGraph. This study provides informative data on the clinical utility of the Fitbit compared with the ActiGraph.

Validating Accelerometry as a Measure of Arm Movement for Children With Hemiplegic Cerebral Palsy

BACKGROUND

For children with hemiplegic cerebral palsy (HCP), rehabilitation aims to increase movement of the affected arm. However, no validated measure objectively examines this construct in pediatric practice or daily life.

OBJECTIVE

The objective of this study was to evaluate the criterion and known-groups validity of accelerometry as a measure of arm movement in children and adolescents with HCP.

DESIGN

This was a prospective cross-sectional study.

METHODS

Twenty-seven children and adolescents with typical development (3.4-13.9 years old) and 11 children and adolescents with HCP (4.7-14.7 years old; Manual Ability Classification System rating I or II) wore accelerometers on their wrists while engaged in 20 minutes of play, which included intermittent intervals of stillness and vigorous movement of the arms. Vector magnitude (VM) values identified the presence (VM > 2.0 counts per epoch) and absence (VM ≤ 2.0 counts per epoch) of arm movement for every 2-second epoch. Video was simultaneously recorded; each 2-second interval of footage was scored as "movement" or "no movement" for each arm.

RESULTS

Agreement between accelerometry and video observation was greater than or equal to 81%, and the prevalence-adjusted and bias-adjusted κ value was greater than or equal to 0.69 for both groups of participants; these results supported the criterion validity of accelerometry. The ratio of nondominant arm movement to dominant arm movement measured by accelerometry was significantly greater in participants with typical development (mean [SD] = 0.87 [0.09]) than in participants with HCP (mean = 0.78 [0.07]) on the basis of 10 age- and sex-matched pairs; these results supported known-groups validity.

LIMITATIONS

The small sample size of the group with HCP prevented the stratification of data by age. Participants with HCP had high or moderately high function of the affected arm; hence, the findings do not apply to children and adolescents with more significant hemiparesis.

CONCLUSIONS

Accelerometry is a valid measure of arm movement in children with HCP and children without HCP. These findings contribute to the development of innovative upper limb assessments for children with hemiparesis.

An accelerometry and observational study to quantify upper limb use after stroke during inpatient rehabilitation

OBJECTIVES

The objectives of the study are to characterize paretic upper limb (UL) use in people with different levels of impairment 4 weeks poststroke and to compare accelerometry and direct observational approaches.

METHODS

Twelve stroke inpatients (five mild, three moderate, and four severe UL impairment) were recruited from a rehabilitation hospital. UL use was measured using accelerometry (24 hr) and direct observation (12 hr of behavioural mapping). Accelerometry variables included duration of use, use ratio, magnitude ratio, bilateral magnitude, and variation ratio. Direct observation recorded the duration of use and type of UL movement (e.g., functional vs. non-functional).

RESULTS

From accelerometry data, people with mild, moderate, and severe UL impairments used their paretic UL 59%, 45%, and 22% of a 24 hr-day, respectively. People with severe UL impairment had the lowest paretic UL use duration (median 1.49 hr/day), magnitude ratio, and variation ratio compared with people with mild and moderate UL impairment. From 12 hr of observational data, people with mild impairment were using their UL for 37.8% of the observed time, whereas the people with moderate and severe impairment were using their UL 15.8% and 4.9%, respectively. UL movements for the mild cohort were mainly functional, whereas UL movements of the moderate and severe cohorts were mainly non-functional. UL movements were predominantly active for the mild and moderate cohorts but passive for the severe cohort. Duration of paretic UL use from accelerometry and observation data were highly correlated (ICC > 0.8), but the absolute percentage error between methods ranged from 34.2% to 42.7%.

CONCLUSIONS

Paretic UL use within the first 4 weeks poststroke differs across levels of impairment in this exploratory study. Accelerometry and observation findings of paretic UL use were correlated and may be needed in different situations as they capture different information.

Laterality of Damage Influences the Relationship Between Impairment and Arm Use After Stroke

OBJECTIVES

To investigate whether the relationship between arm use and motor impairment post-stroke is influenced by the hemisphere of damage.

METHODS

Right-handed patients with unilateral left hemisphere damage (LHD) or right (RHD) (n=58; 28 LHD, 30 RHD) were recruited for this study. The Arm Motor Ability Test and Functional Impact Assessment were used to derive arm use patterns. The Fugl-Meyer motor assessment scale was used to quantify the level of motor impairment.

RESULTS

A significant interaction between patient group and impairment level was observed for contralesional, but not ipsilesional arm use. For lower impairment levels, contralesional (right arm for LHD and left arm for RHD) arm use was greater in LHD than RHD patients. In contrast, for greater levels of impairment, there were no arm use differences between the two patient groups.

CONCLUSIONS

When motor impairment is significant, it overrides potential effects of stroke laterality on the patterns of arm use. However, a robust influence of hemisphere of damage on the patterns of arm use is evident at lower impairment levels. This may be attributed to previously described arm preference effects. These findings suggest adoption of distinct strategies for rehabilitation following left versus right hemisphere damage in right-handers, at least when the impairment is moderate to low. (JINS, 2019, 25, 470-478).

Detection of Pediatric Upper Extremity Motor Activity and Deficits With Accelerometry

IMPORTANCE

Affordable, quantitative methods to screen children for developmental delays are needed. Motor milestones can be an indicator of developmental delay and may be used to track developmental progress. Accelerometry offers a way to gather real-world information about pediatric motor behavior.

OBJECTIVE

To develop a referent cohort of pediatric accelerometry from bilateral upper extremities (UEs) and determine whether movement can accurately distinguish those with and without motor deficits.

DESIGN, SETTING, AND PARTICIPANTS

Children aged 0 to 17 years participated in a prospective cohort from December 8, 2014, to December 29, 2017. Children were recruited from Ranken Jordan Pediatric Bridge Hospital, Maryland Heights, Missouri, and Washington University School of Medicine in St Louis, St Louis, Missouri. Typically developing children were included as a referent cohort if they had no history of motor or neurological deficit; consecutive sampling and matching ensured equal representation of sex and age. Children with diagnosed asymmetric motor deficits were included in the motor impaired cohort.

EXPOSURES

Bilateral UE motor activity was measured using wrist-worn accelerometers for a total of 100 hours in 25-hour increments.

MAIN OUTCOMES AND MEASURES

To characterize bilateral UE motor activity in a referent cohort for the purpose of detecting irregularities in the future, total activity and the use ratio between UEs were used to describe typically developing children. Asymmetric impairment was classified using the mono-arm use index (MAUI) and bilateral-arm use index (BAUI) to quantify the acceleration of unilateral movements.

RESULTS

A total of 216 children enrolled, and 185 children were included in analysis. Of these, 156 were typically developing, with mean (SD) age 9.1 (5.1) years and 81 boys (52.0%). There were 29 children in the motor impaired cohort, with mean (SD) age 7.4 (4.4) years and 16 boys (55.2%). The combined MAUI and BAUI (mean [SD], 0.86 [0.005] and use ratio (mean [SD], 0.90 [0.008]) had similar F1 values. The area under the curve was also similar between the combined MAUI and BAUI (mean [SD], 0.98 [0.004]) and the use ratio (mean [SD], 0.98 [0.004]).

CONCLUSIONS AND RELEVANCE

Bilateral UE movement as measured with accelerometry may provide a meaningful metric of real-world motor behavior across childhood. Screening in early childhood remains a challenge; MAUI may provide an effective method for clinicians to measure and visualize real-world motor behavior in children at risk for asymmetrical deficits.

Hand function and type of grasp used by chronic stroke individuals in actual environment

BACKGROUND

Knowledge of paretic upper limb (UL) use in the actual environment is crucial for defining treatment strategies that are likely to enhance performance.

OBJECTIVE

To quantify the hand function and type of grasp performed in the actual environment following stroke and determine if any differences in hand use are dependent on the degree of motor impairment.

METHOD

This cross-sectional study enrolled 41 participants with chronic hemiparesis classified as having either mild (11), moderate (20), or severe (10) UL impairment. A behavioral map was used while observing hand use over the 4-h experimental period, during which we checked: activity- unimanual, bimanual or non-task-related; hand function- stabilization, manipulation, reach-to-grasp, gesture, support or push; and type of grasp- digital or whole-hand.

RESULTS

Participants with severe impairment did not use the paretic UL spontaneously; analyzing the moderate and mild subgroup together, the predominant UL hand functions were stabilization and manipulation, the paretic UL performs the stabilization function using the whole-hand more frequently (71.2%) than digital (28.8%) grasp. In the subgroup analysis, the paretic and non-paretic UL in the moderate and the paretic UL in the mild subgroup perform the whole-hand stabilization more frequently than digital. Digital grasp is more accomplished by the non-paretic UL in reach-to-grasp hand function, particularly in the mild subgroup.

CONCLUSION

The paretic UL is predominantly employed for stabilization function using a whole-hand grasp. The type of grasp in the actual environment is affected by motor impairment, and greater motor impairment leads to the performance of less complex tasks.

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.