Inertial Sensor Measurements of Upper-Limb Kinematics in Stroke Patients in Clinic and Home Environment

Upper limb assessment with inertial measurement units according to the international classification of functioning in stroke: a systematic review and correlation meta-analysis

OBJECTIVE

To investigate the usefulness of inertial measurement units (IMUs) in the assessment of motor function of the upper limb (UL) in accordance with the international classification of functioning (ICF).

DATA SOURCES

PubMed; Scopus; Embase; WoS and PEDro databases were searched from inception to 1 February 2022.

METHODS

The current systematic review follows PRISMA recommendations. Articles including IMU assessment of UL in stroke individuals have been included and divided into four ICF categories (b710, b735, b760, d445). We used correlation meta-analysis to pool the Fisher Z-score of each correlation between kinematics and clinical assessment.

RESULTS

A total of 35 articles, involving 475 patients, met the inclusion criteria. In the included studies, IMUs have been employed to assess the mobility of joint functions (n = 6), muscle tone functions (n = 4), control of voluntary movement functions (n = 15), and hand and arm use (n = 15). A significant correlation was found in overall meta-analysis based on 10 studies, involving 213 subjects: (r = 0.69) (95% CI: 0.69/0.98; p < 0.001) as in the d445 (r = 0.71) and b760 (r = 0.64) ICF domains, with no heterogeneity across the studies.

CONCLUSION

The literature supports the integration of IMUs and conventional clinical assessment in functional evaluation of the UL after a stroke. The use of a limited number of wearable sensors can provide additional kinematic features of UL in all investigated ICF domains, especially in the ADL tasks when a strong correlation with clinical evaluation was found.

Characterization of Upper Extremity Kinematics Using Virtual Reality Movement Tasks and Wearable IMU Technology

Task-specific training has been shown to be an effective neuromotor rehabilitation intervention, however, this repetitive approach is not always very engaging. Virtual reality (VR) systems are becoming increasingly popular in therapy due to their ability to encourage movement through customizable and immersive environments. Additionally, VR can allow for a standardization of tasks that is often lacking in upper extremity research. Here, 16 healthy participants performed upper extremity movement tasks synced to music, using a commercially available VR game known as Beat Saber. VR tasks were customized to characterize participants' joint angles with respect to each task's specified cardinal direction (inward, outward, upward, or downward) and relative task location (medial, lateral, high, and/or low). Movement levels were designed using three common therapeutic approaches: (1) one arm moving only (unilateral), (2) two arms moving in mirrored directions about the participant's midline (mirrored), or (3) two arms moving in opposing directions about the participant's midline (opposing). Movement was quantified using an XSens System, a wearable inertial measurement unit (IMU) technology. Results reveal a highly engaging and effective approach to quantifying movement strategies. Inward and outward (horizontal) tasks resulted in decreased wrist extension. Upward and downward (vertical) tasks resulted in increased shoulder flexion, wrist radial deviation, wrist ulnar deviation, and elbow flexion. Lastly, compared to opposing, mirrored, and unilateral movement levels often exaggerated joint angles. Virtual reality games, like Beat Saber, offer a repeatable and customizable upper extremity intervention that has the potential to increase motivation in therapeutic applications.

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from B -spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.

Going beyond PA: Assessing sensorimotor capacity with wearables in multiple sclerosis-a cross-sectional study

BACKGROUND

Wearable technologies are currently clinically used to assess energy expenditure in a variety of populations, e.g., persons with multiple sclerosis or frail elderly. To date, going beyond physical activity, deriving sensorimotor capacity instead of energy expenditure, is still lacking proof of feasibility.

METHODS

In this study, we read out sensors (accelerometer and gyroscope) of smartwatches in a sample of 90 persons with multiple sclerosis over the course of one day of everyday life in an inpatient setting. We derived a variety of different kinematic parameters, in addition to lab-based tests of sensorimotor performance, to examine their interrelation by principal component, cluster, and regression analyses.

RESULTS

These analyses revealed three components of behavior and sensorimotor capacity, namely clinical characteristics with an emphasis on gait, gait-related physical activity, and upper-limb related physical activity. Further, we were able to derive four clusters with different behavioral/capacity patterns in these dimensions. In a last step, regression analyses revealed that three selected smartwatch derived kinematic parameters were able to partially predict sensorimotor capacity, e.g., grip strength and upper-limb tapping.

CONCLUSIONS

Our analyses revealed that physical activity can significantly differ between persons with comparable clinical characteristics and that assessments of physical activity solely relying on gait can be misleading. Further, we were able to extract parameters that partially go beyond physical activity, with the potential to be used to monitor the course of disease progression and rehabilitation, or to early identify persons at risk or a sub-clinical threshold of disease severity.

The immersive virtual memory task: Assessing object-location memory in neurological patients using immersive virtual reality

TRIAL REGISTRATION

German Clinical Trials Register identifier: DRKS00024005.

Reliability and Validity of a Wearable Sensing System and Online Gait Analysis Report in Persons after Stroke

The restoration of gait and mobility after stroke is an important and challenging therapy goal due to the complexity of the potentially impaired functions. As a result, precise and clinically feasible assessment methods are required for personalized gait rehabilitation after stroke. The aim of this study is to investigate the reliability and validity of a sensor-based gait analysis system in stroke survivors with different severities of gait deficits. For this purpose, 28 chronic stroke survivors (9 women, ages: 62.04 ± 11.68 years) with mild to moderate walking impairments performed a set of ambulatory assessments (3× 10MWT, 1× 6MWT per session) twice while being equipped with a sensor suit. The derived gait reports provided information about speed, step length, step width, swing and stance phases, as well as joint angles of the hip, knee, and ankle, which we analyzed for test-retest reliability and hypothesis testing. Further, test-retest reliability resulted in a mean ICC of 0.78 (range: 0.46-0.88) for walking 10 m and a mean ICC of 0.90 (range: 0.63-0.99) for walking 6 min. Additionally, all gait parameters showed moderate-to-strong correlations with clinical scales reflecting lower limb function. These results support the applicability of this sensor-based gait analysis system for individuals with stroke-related walking impairments.

Interactive device for the treatment of pediatric neuromotor deficiencies using personalized recovery programs

Abstract: Modern rehabilitation procedures use devices that provide physical therapists with various types of information to improve assessment of patient progress during reha-bilitation plans. The new trend of these technologies is the development of safe, portable and comfortable wearable devices with extensive applications in various environments (medical clinics or at the patient's home). The present work presents a portable and safe device for hand rehabilitation, consisting of five finger force sensors and a palmar sensor arranged in the ball, capable of capturing pressure signals during the execution of move-ments guided by the physiotherapist or by a video game/virtual reality. A 3-axis accel-erometer was used to spatially monitor the patient's movements. A series of games with different levels of difficulty were created, through which the degree of mobility of the pa-tient can be monitored depending on the game he chooses and at the same time reflected by the score obtained at the end of the game. Also, to be more interactive, the interface was chosen to play with 2 players simultaneously. So that they can choose to play in the team or as competitors. The system allows users to show different routines to guide them in their use and also evaluates pressure signals and response time. Keywords: microcontroller, games interactive, physiokinetotherapist, rehabilitation, health im-provement.

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.

Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation

OBJECTIVE

A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients reintegrate safely in the home and community. However, not all patients have the same potential for recovery, making it difficult to set realistic therapy goals and to anticipate future needs for short- or long-term care. The objective of this study was to test the value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation.

METHOD

Supervised machine learning algorithms were trained to classify patients as either household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments.

RESULTS

Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%.

CONCLUSIONS

This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors.

CLINICAL IMPACT

Accurately predicting a patient's functional recovery early in the rehabilitation process would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment.

ARMIA: A Sensorized Arm Wearable for Motor Rehabilitation

In this paper, we present ARMIA: a sensorized arm wearable that includes a combination of inertial and sEMG sensors to interact with serious games in telerehabilitation setups. This device reduces the cost of robotic assistance technologies to be affordable for end-users at home and at rehabilitation centers. Hardware and acquisition software specifications are described together with potential applications of ARMIA in real-life rehabilitation scenarios. A detailed comparison with similar medical technologies is provided, with a specific focus on wearable devices and virtual and augmented reality approaches. The potential advantages of the proposed device are also described showing that ARMIA could provide similar, if not better, the effectivity of physical therapy as well as giving the possibility of home-based rehabilitation.

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.

Quantifying intra- and interlimb use during unimanual and bimanual tasks in persons with hemiparesis post-stroke

Background

Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited.

Objective

To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance.

Methods

Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0–1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM).

Results

A significant group × task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality.

Conclusions

Our algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians.

Hand Motion Analysis during the Execution of the Action Research Arm Test Using Multiple Sensors

The Action Research Arm Test (ARAT) is a standardized outcome measure that can be improved by integrating sensors for hand motion analysis. The purpose of this study is to measure the flexion angle of the finger joints and fingertip forces during the performance of three subscales (Grasp, Grip, and Pinch) of the ARAT, using a data glove (CyberGlove II^®) and five force-sensing resistors (FSRs) simultaneously. An experimental study was carried out with 25 healthy subjects (right-handed). The results showed that the mean flexion angles of the finger joints required to perform the 16 activities were Thumb (Carpometacarpal Joint (CMC) 28.56°, Metacarpophalangeal Joint (MCP) 26.84°, and Interphalangeal Joint (IP) 13.23°), Index (MCP 46.18°, Index Proximal Interphalangeal Joint (PIP) 38.89°), Middle (MCP 47.5°, PIP 42.62°), Ring (MCP 44.09°, PIP 39.22°), and Little (MCP 31.50°, PIP 22.10°). The averaged fingertip force exerted in the Grasp Subscale was 8.2 N, in Grip subscale 6.61 N and Pinch subscale 3.89 N. These results suggest that the integration of multiple sensors during the performance of the ARAT has clinical relevance, allowing therapists and other health professionals to perform a more sensitive, objective, and quantitative assessment of the hand function.

Determinants of Different Aspects of Upper-Limb Activity after Stroke

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

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life.

Characterization of stroke-related upper limb motor impairments across various upper limb activities by use of kinematic core set measures

Background

Upper limb kinematic assessments provide quantifiable information on qualitative movement behavior and limitations after stroke. A comprehensive characterization of spatiotemporal kinematics of stroke subjects during upper limb daily living activities is lacking. Herein, kinematic expressions were investigated with respect to different movement types and impairment levels for the entire task as well as for motion subphases.

Method

Chronic stroke subjects with upper limb movement impairments and healthy subjects performed a set of daily living activities including gesture and grasp movements. Kinematic measures of trunk displacement, shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension, forearm pronation/supination, wrist flexion/extension, movement time, hand peak velocity, number of velocity peaks (NVP), and spectral arc length (SPARC) were extracted for the whole movement as well as the subphases of reaching distally and proximally. The effects of the factors gesture versus grasp movements, and the impairment level on the kinematics of the whole task were tested. Similarities considering the metrics expressions and relations were investigated for the subphases of reaching proximally and distally between tasks and subgroups.

Results

Data of 26 stroke and 5 healthy subjects were included. Gesture and grasp movements were differently expressed across subjects. Gestures were performed with larger shoulder motions besides higher peak velocity. Grasp movements were expressed by larger trunk, forearm, and wrist motions. Trunk displacement, movement time, and NVP increased and shoulder flexion/extension decreased significantly with increased impairment level. Across tasks, phases of reaching distally were comparable in terms of trunk displacement, shoulder motions and peak velocity, while reaching proximally showed comparable expressions in trunk motions. Consistent metric relations during reaching distally were found between shoulder flexion/extension, elbow flexion/extension, peak velocity, and between movement time, NVP, and SPARC. Reaching proximally revealed reproducible correlations between forearm pronation/supination and wrist flexion/extension, movement time and NVP.

Conclusion

Spatiotemporal differences between gestures versus grasp movements and between different impairment levels were confirmed. The consistencies of metric expressions during movement subphases across tasks can be useful for linking kinematic assessment standards and daily living measures in future research and performing task and study comparisons.

Trial registration: ClinicalTrials.gov Identifier NCT03135093. Registered 26 April 2017, https://clinicaltrials.gov/ct2/show/NCT03135093.

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

End-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices. In contrast, we propose a magnetometer-free sensor fusion method. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively; and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ±10 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.

Quantifying Pathological Synergies in the Upper Extremity of Stroke Subjects With the Use of Inertial Measurement Units: A Pilot Study

BACKGROUND

Stroke is one of the main causes of disability in the world, causing loss of motor function on mainly one side of the body. A proper assessment of motor function is required to help to direct and evaluate therapy. Assessment is currently performed by therapists using observer-based standardized clinical assessment protocols. Sensor-based technologies can be used to objectively quantify the presence and severity of motor impairments in stroke patients.

METHODS

In this work, a minimally obstructive distributed inertial sensing system, intended to measure kinematics of the upper extremity, was developed and tested in a pilot study, where 10 chronic stroke subjects performed the arm-related tasks from the Fugl-Meyer Assessment protocol with the affected and non-affected side.

RESULTS

The pilot study showed that the developed distributed measurement system was adequately sensitive to show significant differences in stroke subjects' arm postures between the affected and non-affected side. The presence of pathological synergies can be analysed using the measured joint angles of the upper limb segments, that describe the movement patterns of the subject.

CONCLUSION

Features measured by the system vary from the assessed FMA-UE sub-score showing its potential to provide more detailed clinical information.

Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

BACKGROUND

Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.

METHODS

A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.

RESULTS

Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.

CONCLUSION

This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.

Convolutional neural network in upper limb functional motion analysis after stroke

In this work, implementation of Convolutional Neural Network (CNN) for the purpose of analysis of functional upper limb movement pattern was applied. The main aim of the study was to compare motion of selected activities of daily living of participants after stroke with the healthy ones (in similar age). The optical, marker-based motion capture system was applied for the purpose of data acquisition. There were some attempts made in order to find the existing differences in the motion pattern of the upper limb. For this purpose, the motion features of dominant and non-dominant upper limb of healthy participants were compared with motion features of paresis and non-paresis upper limbs of participants after stroke. On the basis of the newly collected data set, a new CNN application was presented to the classification of motion data in two different class label configurations. Analyzing individual segments of the upper body, it turned out that the arm was the most sensitive segment for capturing changes in the trajectory of the lifting movements of objects.

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Background

Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers.

Objective

In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff.

Methods

We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC).

Results

The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification.

Conclusions

The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

Assessment of Upper Limb Movement Impairments after Stroke Using Wearable Inertial Sensing

Precise and objective assessments of upper limb movement quality after strokes in functional task conditions are an important prerequisite to improve understanding of the pathophysiology of movement deficits and to prove the effectiveness of interventions. Herein, a wearable inertial sensing system was used to capture movements from the fingers to the trunk in 10 chronic stroke subjects when performing reach-to-grasp activities with the affected and non-affected upper limb. It was investigated whether the factors, tested arm, object weight, and target height, affect the expressions of range of motion in trunk compensation and flexion-extension of the elbow, wrist, and finger during object displacement. The relationship between these metrics and clinically measured impairment was explored. Nine subjects were included in the analysis, as one had to be excluded due to defective data. The tested arm and target height showed strong effects on all metrics, while an increased object weight showed effects on trunk compensation. High inter- and intrasubject variability was found in all metrics without clear relationships to clinical measures. Relating all metrics to each other resulted in significant negative correlations between trunk compensation and elbow flexion-extension in the affected arm. The findings support the clinical usability of sensor-based motion analysis.

Augmented Reality-Based Rehabilitation of Gait Impairments: Case Report

Background

Gait and balance impairments are common in neurological diseases, including stroke, and negatively affect patients’ quality of life. Improving balance and gait are among the main goals of rehabilitation. Rehabilitation is mainly performed in clinics, which lack context specificity; therefore, training in the patient’s home environment is preferable. In the last decade, developed rehabilitation technologies such as virtual reality and augmented reality (AR) have enabled gait and balance training outside clinics. Here, we propose a new method for gait rehabilitation in persons who have had a stroke in which mobile AR technology and a sensor-based motion capture system are combined to provide fine-grained feedback on gait performance in real time.

Objective

The aims of this study were (1) to investigate manipulation of the gait pattern of persons who have had a stroke based on virtual augmentation during overground walking compared to walking without AR performance feedback and (2) to investigate the usability of the AR system.

Methods

We developed the ARISE (Augmented Reality for gait Impairments after StrokE) system, in which we combined a development version of HoloLens 2 smart glasses (Microsoft Corporation) with a sensor-based motion capture system. One patient with chronic minor gait impairment poststroke completed clinical gait assessments and an AR parkour course with patient-centered performance gait feedback. The movement kinematics during gait as well as the usability and safety of the system were evaluated.

Results

The patient changed his gait pattern during AR parkour compared to the pattern observed during the clinical gait assessments. He recognized the virtual objects and ranked the usability of the ARISE system as excellent. In addition, the patient stated that the system would complement his standard gait therapy. Except for the symptom of exhilaration, no adverse events occurred.

Conclusions

This project provided the first evidence of gait adaptation during overground walking based on real-time feedback through visual and auditory augmentation. The system has potential to provide gait and balance rehabilitation outside the clinic. This initial investigation of AR rehabilitation may aid the development and investigation of new gait and balance therapies.

Depth Sensor-Based Assessment of Reachable Work Space for Visualizing and Quantifying Paretic Upper Extremity Motor Function in People With Stroke

BACKGROUND

Quantitative evaluation of upper extremity (UE) motor function is important in people with hemiparetic stroke. A depth sensor-based assessment of reachable work space (RWS) was applied to visualize and quantify paretic UE motor function.

OBJECTIVE

The objectives of this study were to examine the characteristics of RWS and to assess its validity, reliability, measurement error, and responsiveness in people with hemiparetic stroke.

DESIGN

This was a descriptive, repeated-measures, observational study.

METHODS

Fifty-eight people with stroke participated. RWS was assessed on both paretic and nonparetic UEs, and the RWS ratio was determined by dividing the RWS of the paretic UE by that of the nonparetic UE. The concurrent validity of the RWS was determined by examining the relationship with the Fugl-Meyer Assessment UE motor score. Test-retest reproducibility was examined in 40 participants. Responsiveness was determined by examining the RWS results before and after 3 weeks of intensive training of the paretic UE in 32 participants.

RESULTS

The lower area of RWS bordering shoulder was significantly larger than the upper area, and the medial-lower area of RWS bordering shoulder was significantly larger than the lateral-lower area. The RWS ratio was highly correlated with the Fugl-Meyer Assessment UE motor score (r = 0.81). The RWS ratio showed good intrarater relative reliability (intraclass correlation coefficient = 0.94) and no fixed or proportional bias. The minimal detectable change of the RWS ratio was 16.6. The responsiveness of the RWS ratio was large (standardized response mean = 0.83).

LIMITATIONS

Interexaminer reliability was not assessed.

CONCLUSIONS

The RWS assessment showed sufficient validity, reliability, and responsiveness in people with hemiparetic stroke. A depth sensor-based RWS evaluation is useful for visualizing and quantifying paretic UE motor function in the clinical setting.

A functional analysis-based approach to quantify upper limb impairment level in chronic stroke patients: a pilot study

The accurate assessment of upper limb motion impairment induced by stroke - which represents one of the primary causes of disability world-wide - is the first step to successfully monitor and guide patients' recovery. As of today, the majority of the procedures relies on clinical scales, which are mostly based on ordinal scaling, operator-dependent, and subject to floor and ceiling effects. In this work, we intend to overcome these limitations by proposing a novel approach to analytically evaluate the level of pathological movement coupling, based on the quantification of movement complexity. To this goal, we consider the variations of functional Principal Components applied to the reconstruction of joint angle trajectories of the upper limb during daily living task execution, and compared these variations between two conditions, i.e. the affected and non-affected arm. A Dissimilarity Index, which codifies the severity of the upper limb motor impairment with respect to the movement complexity of the non-affected arm, is then proposed. This methodology was validated as a proof of concept upon a set of four chronic stroke subjects with mild to moderate arm and hand impairments. As a first step, we evaluated whether the derived outcomes differentiate between the two conditions upon the whole data-set. Secondly, we exploited this concept to discern between different subjects and impairment levels. Results show that: i) differences in terms of movement variability between the affected and nonaffected upper limb are detectable and ii) different impairment profiles can be characterized for single subjects using the proposed approach. Although provisional, these results are very promising and suggest this approach as a basis ingredient for the definition of a novel, operator-independent, sensitive, intuitive and widely applicable scale for the evaluation of upper limb motion impairment.