Estimating Fugl-Meyer clinical scores in stroke survivors using wearable sensors

Detection and Assessment of Point-to-Point Movements During Functional Activities Using Deep Learning and Kinematic Analyses of the Stroke-Affected Wrist

Stoke is a leading cause of long-term disability, including upper-limb hemiparesis. Frequent, unobtrusive assessment of naturalistic motor performance could enable clinicians to better assess rehabilitation effectiveness and monitor patients' recovery trajectories. We therefore propose and validate a two-phase data analytic pipeline to estimate upper-limb impairment based on the naturalistic performance of activities of daily living (ADLs). Eighteen stroke survivors were equipped with an inertial sensor on the stroke-affected wrist and performed up to four ADLs in a naturalistic manner. Continuous inertial time series were segmented into sliding windows, and a machine-learned model identified windows containing instances of point-to-point (P2P) movements. Using kinematic features extracted from the detected windows, a subsequent model was used to estimate upper-limb motor impairment, as measured by the Fugl-Meyer Assessment (FMA). Both models were evaluated using leave-one-subject-out cross-validation. The P2P movement detection model had an area under the precision-recall curve of 0.72. FMA estimates had a normalized root mean square error of 18.8% with R2=0.72. These promising results support the potential to develop seamless, ecologically valid measures of real-world motor performance.

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor

BACKGROUND

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems.

METHOD

We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method.

RESULTS

Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%.

CONCLUSION

Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

Exercise repetition rate measured with simple sensors at home can be used to estimate Upper Extremity Fugl-Meyer score after stroke

Introduction

It would be valuable if home-based rehabilitation training technologies could automatically assess arm impairment after stroke. Here, we tested whether a simple measure-the repetition rate (or "rep rate") when performing specific exercises as measured with simple sensors-can be used to estimate Upper Extremity Fugl-Meyer (UEFM) score.

Methods

41 individuals with arm impairment after stroke performed 12 sensor-guided exercises under therapist supervision using a commercial sensor system comprised of two pucks that use force and motion sensing to measure the start and end of each exercise repetition. 14 of these participants then used the system at home for three weeks.

Results

Using linear regression, UEFM score was well estimated using the rep rate of one forward-reaching exercise from the set of 12 exercises (r2 = 0.75); this exercise required participants to alternately tap pucks spaced about 20 cm apart (one proximal, one distal) on a table in front of them. UEFM score was even better predicted using an exponential model and forward-reaching rep rate (Leave One Out Cross Validation (LOOCV) r2 = 0.83). We also tested the ability of a nonlinear, multivariate model (a regression tree) to predict UEFM, but such a model did not improve prediction (LOOCV r2 = 0.72). However, the optimal decision tree also used the forward-reaching task along with a pinch grip task to subdivide more and less impaired patients in a way consistent with clinical intuition. At home, rep rate for the forward-reaching exercise well predicted UEFM score using an exponential model (LOOCV r2 = 0.69), but only after we re-estimated coefficients using the home data.

Discussion

These results show how a simple measure-exercise rep rate measured with simple sensors-can be used to infer an arm impairment score and suggest that prediction models should be tuned separately for the clinic and home environments.

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

Attempt to Make the Upper-Limb Item of Objective Fugl-Meyer Assessment Using 9-Axis Motion Sensors

The Fugl-Meyer Assessment (FMA) has been used as a functional assessment of upper-limb function in stroke patients. This study aimed to create a more objective and standardized evaluation based on an FMA of the upper-limb items. A total of 30 first-ever stroke patients (65.3 ± 10.3 years old) and 15 healthy participants (35.4 ± 13.4 years old) admitted to Itami Kousei Neurosurgical Hospital were included. A nine-axis motion sensor was attached to the participants, and the joint angles of 17 upper-limb items (excluding fingers) and 23 FMA upper-limb items (excluding reflexes and fingers) were measured. From the measurement results, we analyzed the time-series data of each movement and obtained the correlation between the joint angles of each part. Discriminant analysis showed that 17 and 6 items had a concordance rate of ≥80% (80.0~95.6%) and <80% (64.4~75.6%), respectively. In the multiple regression analysis of continuous variables of FMA, a good regression model was obtained to predict the FMA with three to five joint angles. The discriminant analysis for 17 evaluation items suggests the possibility of roughly calculating FMA scores from joint angles.

Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study

Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.

Upper EXTremity Examination for Neuromuscular Diseases (U-EXTEND): Protocol for multi-modal feasibility study (Preprint)

Background

Neuromuscular diseases, such as spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD), may result in the loss of motor movements, respiratory failure, and early mortality in young children and in adulthood. With novel treatments now available, new evaluation methods are needed to assess progress that is not currently captured in existing motor scale tests.

Objective

With our feasibility study, our interdisciplinary team of investigators aims to develop a novel, multimodal paradigm of measuring motor function in children with neuromuscular diseases that will revolutionize the way that clinical trial end points are measured, thereby accelerating the pipeline of new treatments for childhood neuromuscular diseases. Through the Upper Extremity Examination for Neuromuscular Diseases (U-EXTEND) study, we hypothesize that the novel objective measures of upper extremity muscle structure and function proposed herein will be able to capture small changes and differences in function that cannot be measured with current clinical metrics.

Methods

U-EXTEND introduces a novel paradigm in which concrete, quantitative measures are used to assess motor function in patients with SMA and DMD. Aim 1 will focus on the use of ultrasound techniques to study muscle size, quality, and function, specifically isolating the biceps and pronator muscles of the upper extremities for follow-ups over time. To achieve this, clinical investigators will extract a set of measurements related to muscle structure, quality, and function by using ultrasound imaging and handheld dynamometry. Aim 2 will focus on leveraging wearable wireless sensor technology to capture motion data as participants perform activities of daily living. Measurement data will be examined and compared to those from a healthy cohort, and a motor function score will be calculated.

Results

Data collection for both aims began in January 2021. As of July 2022, we have enrolled 44 participants (9 with SMA, 20 with DMD, and 15 healthy participants). We expect the initial results to be published in summer 2022.

Conclusions

We hypothesize that by applying the described tools and techniques for measuring muscle structure and upper extremity function, we will have created a system for the precise quantification of changes in motor function among patients with neuromuscular diseases. Our study will allow us to track the minimal clinically important difference over time to assess progress in novel treatments. By comparing the muscle scores and functional scores over multiple visits, we will be able to detect small changes in both the ability of the participants to perform the functional tasks and their intrinsic muscle properties.

International Registered Report Identifier (IRRID)

DERR1-10.2196/40856

Quantitative Assessment of Hand Function in Healthy Subjects and Post-Stroke Patients with the Action Research Arm Test

The Action Research Arm Test (ARAT) can provide subjective results due to the difficulty assessing abnormal patterns in stroke patients. The aim of this study was to identify joint impairments and compensatory grasping strategies in stroke patients with left (LH) and right (RH) hemiparesis. An experimental study was carried out with 12 patients six months after a stroke (three women and nine men, mean age: 65.2 ± 9.3 years), and 25 healthy subjects (14 women and 11 men, mean age: 40.2 ± 18.1 years. The subjects were evaluated during the performance of the ARAT using a data glove. Stroke patients with LH and RH showed significantly lower flexion angles in the MCP joints of the Index and Middle fingers than the Control group. However, RH patients showed larger flexion angles in the proximal interphalangeal (PIP) joints of the Index, Middle, Ring, and Little fingers. In contrast, LH patients showed larger flexion angles in the PIP joints of the Middle and Little fingers. Therefore, the results showed that RH and LH patients used compensatory strategies involving increased flexion at the PIP joints for decreased flexion in the MCP joints. The integration of a data glove during the performance of the ARAT allows the detection of finger joint impairments in stroke patients that are not visible from ARAT scores. Therefore, the results presented are of clinical relevance.

Poststroke Grasp Ability Assessment using an Intelligent Data Glove based on Action Research Arm Test: Development, Algorithms, and Experiments

Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.

Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment

In rehabilitation, the Fugl-Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.

Kinematic Evaluation via Inertial Measurement Unit Associated with Upper Extremity Motor Function in Subacute Stroke: A Cross-Sectional Study

Kinematic evaluation via portable sensor system has been increasingly applied in neurological sciences and clinical practice. However, conventional kinematic evaluation rarely extends the context beyond the motor impairment level. In addition, kinematic tasks with numerous items could be complex and time consuming that pose a burden to test applications and data processing. The study aimed to explore the correlation of finger-to-nose task (FNT) kinematics via Inertial Measurement Unit with upper limb motor function in subacute stroke. In this study, six FNT kinematic variables were used to measure movement time, smoothness, and velocity in 37 participants with subacute stroke. Upper limb motor function was evaluated with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and modified Barthel Index (MBI). As a result, mean velocity, peak velocity, and the number of movement units were associated with the clinical assessments. The multivariable linear regression models could estimate 55%, 51%, and 32% of variance in FMA-UE, ARAT, and MBI, respectively. In addition, age, gender, type of stroke, and paretic side had no significant effects on these associations. Results show that FNT kinematic variables measured via Inertial Measurement Unit are associated with upper extremity motor function in individuals with subacute stroke. The objective kinematic evaluation may be suitable for predicting clinical measures of motor impairment and capacity to understand upper extremity motor recovery and clinical decision making after stroke. This trial is registered with ChiCTR1900026656.

NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl-Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

SIGNIFICANCE

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

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

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

Can kinematic parameters of 3D reach-to-target movements be used as a proxy for clinical outcome measures in chronic stroke rehabilitation? An exploratory study

BACKGROUND

Despite numerous trials investigating robot-assisted therapy (RT) effects on upper-extremity (UE) function after stroke, few have explored the relationship between three-dimensional (3D) reach-to-target kinematics and clinical outcomes. The objectives of this study were to 1) investigate the correlation between kinematic parameters of 3D reach-to-target movements and UE clinical outcome measures, and 2) examine the degree to which differences in kinematic parameters across individuals can account for differences in clinical outcomes in response to RT.

METHODS

Ten chronic stroke survivors participated in a pilot RT intervention (eighteen 1-h sessions) integrating cognitive skills training and a home-action program. Clinical outcome measures and kinematic parameters of 3D reach-to-target movements were collected pre- and post-intervention. The correlation between clinical outcomes and kinematic parameters was investigated both cross-sectionally and longitudinally (i.e., changes in response to the intervention). Changes in clinical outcomes and kinematic parameters were tested for significance in both group and subject-by-subject analyses. Potential associations between individual differences in kinematic parameters and differences in clinical outcomes were examined.

RESULTS

Moderate-to-strong correlation was found between clinical measures and specific kinematic parameters when examined cross-sectionally. Weaker correlation coefficients were found longitudinally. Group analyses revealed significant changes in clinical outcome measures in response to the intervention; no significant group changes were observed in kinematic parameters. Subject-by-subject analyses revealed changes with moderate-to-large effect size in the kinematics of 3D reach-to-target movements pre- vs. post-intervention. Changes in clinical outcomes and kinematic parameters varied widely across participants.

CONCLUSIONS

Large variability was observed across subjects in response to the intervention. The correlation between changes in kinematic parameters and clinical outcomes in response to the intervention was variable and not strong across parameters, suggesting no consistent change in UE motor strategies across participants. These results highlight the need to investigate the response to interventions at the individual level. This would enable the identification of clusters of individuals with common patterns of change in response to an intervention, providing an opportunity to use cluster-specific kinematic parameters as a proxy of clinical outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02747433 . Registered on April 21st, 2016.

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.

A review of computational approaches for evaluation of rehabilitation exercises

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches. The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.

Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task

Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% ( r²=0.70 ) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors' rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.

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.

Development of Sensor-Based Measures of Upper Extremity Interlimb Coordination

The development of motor impairment after the onset of an injury such as stroke may result in long-term compensatory behaviors. Because compensation often evolves in ambient settings (outside the purview of monitoring clinicians), there is a need for quantitative tools capable of accurately detecting the subtleties of compensation and related reduction in interlimb coordination. Improvement in interlimb coordination may serve as a marker of recovery from stroke, and rehabilitation progress. The current study investigates measures of upper extremity interlimb coordination in persons post-stroke and healthy controls. It introduces a novel algorithm for objective characterization of interlimb coordination during the performance of real-world tasks.

Cellphone-Based Automated Fugl-Meyer Assessment to Evaluate Upper Extremity Motor Function After Stroke

The Fugl-Meyer Assessment (FMA) is a widely used evaluation tool for assessing upper extremity motor function during stroke rehabilitation. However, the FMA is a repetitive, time-consuming task that currently must be performed by therapists in a hospital or clinic. We thus propose an alternative automated approach in which patients perform FMA movements while holding a cellphone at the hand and receive automated FMA scores. In the proposed system, features are extracted from cellphone movement data and decision trees are used to automatically score FMA test items. Ten stroke patients with upper extremity dysfunction participated in a validation experiment to compare automated FMA scores with traditional FMA scores from a trained therapist. Results showed that FMA scores from the cellphone-based automated system were highly correlated with FMA scores from the trained therapist (r² = 0.97), and that the average accuracy for individual FMA test items was 85%. These results demonstrate that such a portable, automated FMA system could potentially be used to assess upper extremity function during stroke rehabilitation to remove the repetitive, time-consuming burden from therapists and could potentially be performed in clinic or home-based settings.

Relative Efficacy of Sensor Modalities for Estimating Post-Stroke Motor Impairment

Wearable inertial sensing has been beneficial in the development of measures of motor impairment after stroke. While most early work focused on the use of accelerometry, recent work has increasingly shown that rate gyroscopes may provide complementary information. Differences in performance of accelerometers and gyroscopes in activity recognition may be due to the nature of the impairment. The current approach seeks to investigate the relative sensitivity of these sensor modalities to impairment by evaluating their classification accuracy for tasks adapted from the Fugl-Meyer Assessment. Our findings indicated that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively).

Using Inertial Measurement Units and Electromyography to Quantify Movement during Action Research Arm Test Execution

In patients after stroke, ability of the upper limb is commonly assessed with standardised clinical tests that provide a complete upper limb assessment. This paper presents quantification of upper limb movement during the execution of Action research arm test (ARAT) using a wearable system of inertial measurement units (IMU) for kinematic quantification and electromyography (EMG) sensors for muscle activity analysis. The test was executed with each arm by a group of healthy subjects and a group of patients after stroke allocated into subgroups based on their clinical scores. Tasks were segmented into movement and manipulation phases. Each movement phase was quantified with a set of five parameters: movement time, movement smoothness, hand trajectory similarity, trunk stability, and muscle activity for grasping. Parameters vary between subject groups, between tasks, and between task phases. Statistically significant differences were observed between patient groups that obtained different clinical scores, between healthy subjects and patients, and between the unaffected and the affected arm unless the affected arm shows normal performance. Movement quantification enables differentiation between different subject groups within movement phases as well as for the complete task. Spearman's rank correlation coefficient shows strong correlations between patient's ARAT scores and movement time as well as movement smoothness. Weak to moderate correlations were observed for parameters that describe hand trajectory similarity and trunk stability. Muscle activity correlates well with grasping activity and the level of grasping force in all groups.

Using an interactive virtual environment to integrate a digital Action Research Arm Test, motor imagery and action observation to assess and improve upper limb motor function in patients with neuromuscular impairments: a usability and feasibility study protocol

INTRODUCTION

In the recent past, training systems using an interactive virtual environment have been introduced to neurorehabilitation. Such systems can be applied to encourage purposeful limb movements and will increasingly be used at home by the individual patient. Therefore, an integrated valid and reliable assessment tool on the basis of such a system to monitor the recovery process would be an essential asset.

OBJECTIVES

The aim of the study is to evaluate usability, feasibility and validity of the digital version of the Action Research Arm Test using the Bi-Manu-Trainer system as a platform. Additionally, the feasibility and usability of the implementation of action observation and motor imagery tasks into the Bi-Manu-Trainer software will be evaluated.

PATIENTS AND METHODS

This observational study is planned as a single-arm trial for testing the new assessment and the action observation and motor imagery training module. Therefore, 75 patients with Parkinson's disease, multiple sclerosis, stroke, traumatic brain injury or Guillain-Barré syndrome will be included. 30 out of the 75 patients will additionally take part in a 4-week training on the enhanced Bi-Manu-Trainer system. Primary outcomes will be the score on the System Usability Scale and the correlation between the conventional and digital Action Research Arm Test scores. Secondary outcomes will be hand dexterity, upper limb activities of daily living and quality of life.

HYPOTHESIS

We hypothesise that the digital Action Research Arm Test assessment is a valid and essential tool and that it is feasible to incorporate action observation and motor imagery into Bi-Manu-Trainer practice. The results are expected to give recommendations for necessary modifications and might also contribute knowledge concerning the application of action observation and motor imagery tasks using a training system such as the Bi-Manu-Trainer.

TRIAL REGISTRATION NUMBER

NCT03268304; Pre-results.

Longitudinal Walking Analysis in Hemiparetic Patients Using Wearable Motion Sensors: Is There Convergence Between Body Sides?

Background: Longitudinal movement parameter analysis of hemiparetic patients over several months could reveal potential recovery trends and help clinicians adapting therapy strategies to maximize recovery outcome. Wearable sensors offer potential for day-long movement recordings in realistic rehabilitation settings including activities of daily living, e.g., walking. The measurement of walking-related movement parameters of affected and non-affected body sides are of interest to determine mobility and investigate recovery trends. Methods: By comparing movement of both body sides, recovery trends across the rehabilitation duration were investigated. We derived and validated selected walking segments from free-living, day-long movement by using rules that do not require data-based training or data annotations. Automatic stride segmentation using peak detection was applied to walking segments. Movement parameters during walking were extracted, including stride count, stride duration, cadence, and sway. Finally, linear regression models over each movement parameter were derived to forecast the moment of convergence between body sides. Convergence points were expressed as duration and investigated in a patient observation study. Results: Convergence was analyzed in walking-related movement parameters in an outpatient study including totally 102 full-day recordings of inertial movement data from 11 hemiparetic patients. The recordings were performed over several months in a day-care centre. Validation of the walking extraction method from sensor data yielded sensitivities up to 80 % and specificity above 94 % on average. Comparison of automatically and manually derived movement parameters showed average relative errors below 6 % between affected and non-affected body sides. Movement parameter variability within and across patients was observed and confirmed by case reports, reflecting individual patient behavior. Conclusion: Convergence points were proposed as intuitive metric, which could facilitate training personalization for patients according to their individual needs. Our continuous movement parameter extraction and analysis, was feasible for realistic, day-long recordings without annotations. Visualizations of movement parameter trends and convergence points indicated that individual habits and patient therapies were reflected in walking and mobility. Context information of clinical case reports supported trend and convergence interpretation. Inconsistent convergence point estimation suggested individually varying deficiencies. Long-term recovery monitoring using convergence points could support patient-specific training strategies in future remote rehabilitation.

Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c$ \end{document}-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$F$ \end{document}-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models

Background: Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed.

Objectives: We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary.

Methods: We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines.

Results: Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines.

Conclusion: Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.

A method to qualitatively assess arm use in stroke survivors in the home environment

Wearable sensor technology has enabled unobtrusive monitoring of arm movements of stroke survivors in the home environment. However, the most widely established method, based on activity counts, provides quantitative rather than qualitative information on arm without functional insights, and is sensitive to passive arm movements during ambulatory activities. We propose a method to quantify functionally relevant arm use in stroke survivors relying on a single wrist-worn inertial measurement unit. Orientation of the forearm during movements is measured in order identify gross arm movements. The method is validated in 10 subacute/chronic stroke survivors wearing inertial sensors at 5 anatomical locations for 48 h. Measurements are compared to conventional activity counts and to a test for gross manual dexterity. Duration of gross arm movements of the paretic arm correlated significantly better with the Box and Block Test ([Formula: see text]) than conventional activity counts when walking phases were included ([Formula: see text]), and similar results were found when comparing ratios of paretic and non-paretic arms for gross movements and activity counts. The proposed gross arm movement metric is robust against passive arm movements during ambulatory activities and requires only a single-sensor module placed at the paretic wrist for the assessment of functionally relevant arm use.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

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

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.

Component-Level Tuning of Kinematic Features From Composite Therapist Impressions of Movement Quality

In this paper, we propose a general framework for tuning component-level kinematic features using therapists' overall impressions of movement quality, in the context of a home-based adaptive mixed reality rehabilitation (HAMRR) system. We propose a linear combination of nonlinear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overall movement quality provided by a therapist. The kinematic features are chosen such that they correlate with the quality of wrist movements to clinical assessment scores. Further, the proposed features are designed to be reliably extracted from an inexpensive and portable motion capture system using a single reflective marker on the wrist. Using a dataset collected from ten stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment in HAMRR systems. The system is currently being deployed for large-scale evaluations, and will represent an increasingly important application area of motion capture and activity analysis.

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

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

Objective Assessment of Upper-Limb Mobility for Poststroke Rehabilitation

The assessment of the limb mobility of stroke patients is an essential part of poststroke rehabilitation. Conventionally, the assessment is manually performed by clinicians using chart-based ordinal scales, which can be subjective and inefficient. By introducing quantitative evaluation measures, the sensitivity and efficiency of the assessment process can be significantly improved. In this paper, a novel single-index-based assessment approach for quantitative upper-limb mobility evaluation has been proposed for poststroke rehabilitation. Instead of the traditional human-observation-based measures, the proposed assessment system utilizes the kinematic information automatically collected during a regular rehabilitation training exercise using a wearable inertial measurement unit. By calculating a single index, the system can efficiently generate objective and consistent quantitative results that can reflect the stroke patient's upper-limb mobility. In order to verify and validate the proposed assessment system, experiments have been conducted using 145 motion samples collected from 21 stroke patients (12 males, nine females, mean age 58.7±19.3) and eight healthy participants. The results have suggested that the proposed assessment index can not only differentiate the levels of limb function impairment clearly (p < 0.001, two-tailed Welch's t-test), but also strongly correlate with the Brunnstrom stages of recovery (r = 0.86, p < 0.001). The assessment index is also proven to have great potential in automatic Brunnstrom stage classification application with an 82.1% classification accuracy, while using a K-nearest-neighbor classifier.

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a 7-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported.

Robot-assisted arm assessments in spinal cord injured patients: a consideration of concept study

Robotic assistance is increasingly used in neurological rehabilitation for enhanced training. Furthermore, therapy robots have the potential for accurate assessment of motor function in order to diagnose the patient status, to measure therapy progress or to feedback the movement performance to the patient and therapist in real time. We investigated whether a set of robot-based assessments that encompasses kinematic, kinetic and timing metrics is applicable, safe, reliable and comparable to clinical metrics for measurement of arm motor function. Twenty-four healthy subjects and five patients after spinal cord injury underwent robot-based assessments using the exoskeleton robot ARMin. Five different tasks were performed with aid of a visual display. Ten kinematic, kinetic and timing assessment parameters were extracted on joint- and end-effector level (active and passive range of motion, cubic reaching volume, movement time, distance-path ratio, precision, smoothness, reaction time, joint torques and joint stiffness). For cubic volume, joint torques and the range of motion for most joints, good inter- and intra-rater reliability were found whereas precision, movement time, distance-path ratio and smoothness showed weak to moderate reliability. A comparison with clinical scores revealed good correlations between robot-based joint torques and the Manual Muscle Test. Reaction time and distance-path ratio showed good correlation with the “Graded and Redefined Assessment of Strength, Sensibility and Prehension” (GRASSP) and the Van Lieshout Test (VLT) for movements towards a predefined position in the center of the frontal plane. In conclusion, the therapy robot ARMin provides a comprehensive set of assessments that are applicable and safe. The first results with spinal cord injured patients and healthy subjects suggest that the measurements are widely reliable and comparable to clinical scales for arm motor function. The methods applied and results can serve as a basis for the future development of end-effector and exoskeleton-based robotic assessments.

Spectral analyses of wrist motion in individuals poststroke: the development of a performance measure with promise for unsupervised settings

BACKGROUND

Upper extremity use in daily life is a critical ingredient of continued functional recovery after cerebral stroke. However, time-evolutions of use-dependent motion quality are poorly understood due to limitations of existing measurement tools.

OBJECTIVE

Proof-of-concept study to determine if spectral analyses explain the variability of known temporal kinematic movement quality (ie, movement duration, number of peaks, jerk) for uncontrolled reach-to-grasp tasks.

METHODS

Ten individuals with chronic stroke performed unimanual goal-directed movements using both hands, with and without task object present, wearing accelerometers on each wrist. Temporal and spectral measures were extracted for each gesture. The effects of performance condition on outcome measures were determined using 2-way, within subject, hand (nonparetic vs paretic) × object (present vs absent) analysis of variance. Regression analyses determined if spectral measures explained the variability of the temporal measures.

RESULTS

There were main effects of hand on all 3 temporal measures and main effects of object on movement duration and peaks. For the paretic limb, spectral measures explain 41.2% and 51.1% of the variability in movement duration and peaks, respectively. For the nonparetic limb, spectral measures explain 40.1%, 42.5%, and 27.8% of the variability of movement duration, peaks, and jerk, respectively.

CONCLUSIONS

Spectral measures explain the variability of motion efficiency and control in individuals with stroke. Signal power from 1.0 to 2.0 Hz is sensitive to changes in hand and object. Analyzing the evolution of this measure in ambient environments may provide as yet uncharted information useful for evaluating long-term recovery.

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

BACKGROUND

Rehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients' feedback and experts' observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.

METHODS

We performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.

RESULTS

We achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p < 0.05 for all tasks) between our regression estimate of motor task capacity and experts' ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.

CONCLUSIONS

We found that children's task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children's motor performance in daily life with a follow-up study.