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

Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors' Motor Ability

BACKGROUND

Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients' motor capacity.

OBJECTIVE

To explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist.

METHODS

Seventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity.

RESULTS

We have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson's correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and -.68 with the Performance Time component of the Wolf Motor Function Test).

CONCLUSIONS

The findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors' motor progression throughout rehabilitation.

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.

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.

Associations Between Coordination and Wearable Sensor Variables Vary by Recording Context but Not Assessment Type

Motor coordination is an important driver of development and improved coordination assessments could facilitate better screening, diagnosis, and intervention for children at risk of developmental disorders. Wearable sensors could provide data that enhance the characterization of coordination and the clinical utility of that data may vary depending on how sensor variables from different recording contexts relate to coordination. We used wearable sensors at the wrists to capture upper-limb movement in 85 children aged 6-12. Sensor variables were extracted from two recording contexts. Structured recordings occurred in the lab during a unilateral throwing task. Unstructured recordings occurred during free-living activity. The objective was to determine the influence of recording context (unstructured versus structured) and assessment type (direct vs. indirect) on the association between sensor variables and coordination. The greatest associations were between six sensor variables from the structured context and the direct measure of coordination. Worse coordination scores were associated with upper-limb movements that had higher peak magnitudes, greater variance, and less smoothness. The associations were consistent across both arms, even though the structured task was unilateral. This finding suggests that wearable sensors could be paired with a simple, structured task to yield clinically informative variables that relate to motor coordination.

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.

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.

Assessing Smoothness of Arm Movements With Jerk: A Comparison of Laterality, Contraction Mode and Plane of Elevation. A Pilot Study

Measuring the quality of movement is a need and a challenge for clinicians. Jerk, defined as the quantity of acceleration variation, is a kinematic parameter used to assess the smoothness of movement. We aimed to assess and compare jerk metrics in asymptomatic participants for 3 important movement characteristics that are considered by clinicians during shoulder examination: dominant and non-dominant side, concentric and eccentric contraction mode, and arm elevation plane. In this pilot study, we measured jerk metrics by using Xsens^® inertial measurement units strapped to the wrists for 11 different active arm movements (ascending and lowering phases): 3 bilateral maximal arm elevations in sagittal, scapular and frontal plane; 2 unilateral functional movements (hair combing and low back washing); and 2 unilateral maximal arm elevations in sagittal and scapular plane, performed with both arms alternately, right arm first. Each arm movement was repeated 3 times successively and the whole procedure was performed 3 times on different days. The recorded time series was segmented with semi-supervised algorithms. Comparisons involved the Wilcoxon signed rank test (p < 0.05) with Bonferroni correction. We included 30 right-handed asymptomatic individuals [17 men, mean (SD) age 31.9 (11.4) years]. Right jerk was significantly less than left jerk for bilateral arm elevations in all planes (all p < 0.05) and for functional movement (p < 0.05). Jerk was significantly reduced during the concentric (ascending) phase than eccentric (lowering) phase for bilateral and unilateral right and left arm elevations in all planes (all p < 0.05). Jerk during bilateral arm elevation was significantly reduced in the sagittal and scapular planes versus the frontal plane (both p < 0.01) and in the sagittal versus scapular plane (p < 0.05). Jerk during unilateral left arm elevation was significantly reduced in the sagittal versus scapular plane (p < 0.05). Jerk metrics did not differ between sagittal and scapular unilateral right arm elevation. Using inertial measurement units, jerk metrics can well describe differences between the dominant and non-dominant arm, concentric and eccentric modes and planes in arm elevation. Jerk metrics were reduced during arm movements performed with the dominant right arm during the concentric phase and in the sagittal plane. Using IMUs, jerk metrics are a promising method to assess the quality of basic shoulder movement.

Measuring Movement Quality of the Stroke-Impaired Upper Extremity with a Wearable Sensor: Toward a Smoothness Metric for Home Rehabilitation Exercise Programs

Remote patient monitoring systems show promise for assisting stroke patients in home exercise programs. While these systems typically measure exercise repetitions in order to monitor compliance, a key goal of therapists is to also monitor movement quality. Here we develop a measure of movement quality - Peak Intensity - that is a measure of movement smoothness that is implementable with a wrist-worn inertial measurement unit (IMU) in the context of performing repetitions of an upper extremity exercise. To calculate Peak Intensity, we assume we have an accurate count of the number of exercise repetitions in an exercise set, then calculate Peak Intensity as the total number of movement peaks from the continuous stream of IMU data generated across the set, divided by the number of repetitions. Using wrist-worn IMU measurements from 19 participants with chronic stroke performing a sample exercise in which they picked up and moved blocks across a divider (i.e. the Box and Blocks Test) we show that Peak Intensity is moderately correlated with a widely used measure of movement quality, the Quality of Movement score of the Motor Activity Log. Peak Intensity is also strongly correlated with a measure of hand function (the BBT score itself), but is more sensitive at greater levels of impairment. Finally, we show Peak Intensity can be validly derived from either wrist acceleration or angular velocity. These results suggest Peak Intensity could serve as an indicator of movement exercise quality for therapists monitoring home rehabilitation, and, potentially, as a means to provide augmented feedback to patients about their exercise quality.

A Pilot Study of a Sensor Enhanced Activity Management System for Promoting Home Rehabilitation Exercise Performed during the COVID-19 Pandemic: Therapist Experience, Reimbursement, and Recommendations for Implementation

Adherence to home exercise programs (HEPs) during physical rehabilitation is usually unmonitored and is thought to be low from self-reports. This article describes exploratory implementation of a Sensor Enhanced Activity Management (SEAM) system that combines HEP management software with a movement sensor for monitoring and motivating HEP adherence. The article also presents results from attempting to gain reimbursement for home use of the system with therapist oversight using Remote Physiologic Monitoring (RPM) codes. Four therapists used the system in their regular practice during the first six months of the COVID-19 pandemic. Therapists filled out surveys, kept notes, and participated in interviews. Billing and reimbursement data were obtained from the treatment facility. Exercise data from the SEAM system were used to understand HEP adherence. Patients were active for a mean of 40% (26% SD) of prescribed days and completed a mean of 25% (25% SD) of prescribed exercises. The therapists billed 23 RPM codes (USD 2353), and payers reimbursed eight of those instances (USD 649.21). The therapists reported that remote monitoring and the use of a physical movement sensor was motivating to their patients and increased adherence. Sustained technical support for therapists will likely improve implementation of new remote monitoring and treatment systems. RPM codes may enable reimbursement for review and program management activities, but, despite COVID-19 CMS waivers, organizations may have more success if these services are billed under supervision of a physician.

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Background: Wearable inertial sensor technology (WIST) systems provide feedback, aiming to modify aberrant postures and movements. The literature on the effects of feedback from WIST during work or work-related activities has not been previously summarised. This review examines the effectiveness of feedback on upper body kinematics during work or work-related activities, along with the wearability and a quantification of the kinematics of the related device. Methods: The Cinahl, Cochrane, Embase, Medline, Scopus, Sportdiscus and Google Scholar databases were searched, including reports from January 2005 to July 2021. The included studies were summarised descriptively and the evidence was assessed. Results: Fourteen included studies demonstrated a 'limited' level of evidence supporting posture and/or movement behaviour improvements using WIST feedback, with no improvements in pain. One study assessed wearability and another two investigated comfort. Studies used tri-axial accelerometers or IMU integration (n = 5 studies). Visual and/or vibrotactile feedback was mostly used. Most studies had a risk of bias, lacked detail for methodological reproducibility and displayed inconsistent reporting of sensor technology, with validation provided only in one study. Thus, we have proposed a minimum 'Technology and Design Checklist' for reporting. Conclusions: Our findings suggest that WIST may improve posture, though not pain; however, the quality of the studies limits the strength of this conclusion. Wearability evaluations are needed for the translation of WIST outcomes. Minimum reporting standards for WIST should be followed to ensure methodological reproducibility.

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

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

Quantification of the relative arm use in patients with hemiparesis using inertial measurement units

Introduction

Accelerometry-based activity counting for measuring arm use is prone to overestimation due to non-functional movements. In this paper, we used an inertial measurement unit (IMU)-based gross movement (GM) score to quantify arm use.

Methods

In this two-part study, we first characterized the GM by comparing it to annotated video recordings of 5 hemiparetic patients and 10 control subjects performing a set of activities. In the second part, we tracked the arm use of 5 patients and 5 controls using two wrist-worn IMUs for 7 and 3 days, respectively. The IMU data was used to develop quantitative measures (total and relative arm use) and a visualization method for arm use.

Results

From the characterization study, we found that GM detects functional activities with 50–60% accuracy and eliminates non-functional activities with >90% accuracy. Continuous monitoring of arm use showed that the arm use was biased towards the dominant limb and less paretic limb for controls and patients, respectively.

Conclusions

The gross movement score has good specificity but low sensitivity in identifying functional activity. The at-home study showed that it is feasible to use two IMU-watches to monitor relative arm use and provided design considerations for improving the assessment method.

Clinical trial registry number: CTRI/2018/09/015648

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.

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.

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.

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.

Individualized feedback to change multiple gait deficits in chronic stroke

Background

Walking deficits in people post-stroke are often multiple and idiosyncratic in nature. Limited patient and therapist resources necessitate prioritization of deficits such that some may be left unaddressed. More efficient delivery of therapy may alleviate this challenge. Here, we look to determine the utility of a novel principal component-based visual feedback system that targets multiple, patient-specific features of gait in people post-stroke.

Methods

Ten individuals with stroke received two sessions of visual feedback to attain a walking goal. This goal consisted of bilateral knee and hip joint angles of a typical ‘healthy’ walking pattern. The feedback system uses principal component analysis (PCA) to algorithmically weight each of the input features so that participants received one stream of performance feedback. In the first session, participants had to explore different patterns to achieve the goal, and in the second session they were informed of the goal walking pattern. Ten healthy, age-matched individuals received the same paradigm, but with a hemiparetic goal (i.e. to produce the pattern of an exemplar stroke participant). This was to distinguish the extent to which performance limitations in stroke were due neurological injury or the PCA based visual feedback itself.

Results

Principal component-based visual feedback can differentially bias multiple features of walking toward a prescribed goal. On average, individuals with stroke typically improved performance via increased paretic knee and hip flexion, and did not perform better with explicit instruction. In contrast, healthy people performed better (i.e. could produce the desired exemplar stroke pattern) in both sessions, and were best with explicit instruction. Importantly, the feedback for stroke participants accommodated a heterogeneous set of walking deficits by individually weighting each feature based on baseline walking.

Conclusions

People with and without stroke are able to use this novel visual feedback to train multiple, specific features of gait. Important for stroke, the PCA feedback allowed for targeting of patient-specific deficits. This feedback is flexible to any feature of walking in any plane of movement, thus providing a potential tool for therapists to simultaneously target multiple aberrant features of gait.

Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.

Wearable systems for shoulder kinematics assessment: a systematic review

BACKGROUND

Wearable sensors are acquiring more and more influence in diagnostic and rehabilitation field to assess motor abilities of people with neurological or musculoskeletal impairments. The aim of this systematic literature review is to analyze the wearable systems for monitoring shoulder kinematics and their applicability in clinical settings and rehabilitation.

METHODS

A comprehensive search of PubMed, Medline, Google Scholar and IEEE Xplore was performed and results were included up to July 2019. All studies concerning wearable sensors to assess shoulder kinematics were retrieved.

RESULTS

Seventy-three studies were included because they have fulfilled the inclusion criteria. The results showed that magneto and/or inertial sensors are the most used. Wearable sensors measuring upper limb and/or shoulder kinematics have been proposed to be applied in patients with different pathological conditions such as stroke, multiple sclerosis, osteoarthritis, rotator cuff tear. Sensors placement and method of attachment were broadly heterogeneous among the examined studies.

CONCLUSIONS

Wearable systems are a promising solution to provide quantitative and meaningful clinical information about progress in a rehabilitation pathway and to extrapolate meaningful parameters in the diagnosis of shoulder pathologies. There is a strong need for development of this novel technologies which undeniably serves in shoulder evaluation and therapy.

Jerk Decomposition during Bimanual Independent Arm Cranking

The relationship between the smoothness of the upper limb endpoint movement and multi joint angular motion is a function of the individual joint angular velocities, accelerations, and jerks as well as the instantaneous arm configuration and its rate of change during movement execution. We compared the contribution of jerk components to the total endpoint jerk in able bodied participants who performed arm cranking movements on an arm cranking device where the two arms could crank independently. The results of this investigation suggest that the most dominant components of the end effector jerk are related to both the angular jerks and to the change of arm configuration pose. This jerk partitioning is much stronger as it was found previously for both reaching arm movements and single hand cranking. This shows the task specificity of the decomposition of endpoint jerk and the effect that bi-manual tasks can have on the smoothness of movements. The proposed decomposition may give useful information in why certain bi-manual rehabilitation processes are more useful than others.

Sensor Measures of Symmetry Quantify Upper Limb Movement in the Natural Environment Across the Lifespan

Knowledge of upper limb activity in the natural environment is critical for evaluating the effectiveness of rehabilitation services. Wearable sensors allow efficient collection of these data and have the potential to be less burdensome than self-report measures of activity. Sensors can capture many different variables of activity and daily performance, many of which could be useful in identifying deviation from typical movement behavior or measuring outcomes from rehabilitation interventions. Although it has potential, sensor measurement is just emerging, and there is a lack of consensus regarding which variables of daily performance are valid, sensitive, specific, and useful. We propose that symmetry of full-day upper limb movement is a key variable. We describe here that symmetry is valid, robustly observed within a narrow range across the lifespan in typical development, and shows evidence of being different in populations with neuromotor impairment. Key next steps include the determination of sensitivity, specificity, minimal detectable change, and minimal clinically important change/difference. This information is needed to determine whether an individual belongs to the typical or atypical group, whether change has occurred, and whether that change is beneficial.