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

The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions

In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for more objective outcomes to facilitate tailored rehabilitation interventions following stroke. Emerging technologies, like head-mounted virtual reality (HMD-VR) platforms, have responded to this demand by integrating diverse tracking methodologies. Specifically, HMD-VR technology enables the comprehensive tracking of body posture, encompassing hand position and gesture, facilitated either through specific tracker placements or via integrated cameras coupled with sophisticated computer graphics algorithms embedded within the helmet. This review aims to present the state-of-the-art applications of HMD-VR platforms for kinematic analysis of the upper limb in post-stroke patients, comparing them with conventional tracking systems. Additionally, we address the potential benefits and challenges associated with these platforms. These systems might represent a promising avenue for safe, cost-effective, and portable objective motor assessment within the field of neurorehabilitation, although other systems, including robots, should be taken into consideration.

Kinematic characteristics of elbow joint range of motion in elite Chinese freestyle swimmers with elbow pain during dry-land simulations of swimming strokes

The aim of this study was to obtain quantitative data on elbow joint ROM in elite freestyle swimmers with EP in China. Of the 50 elite freestyle swimmers recruited, 41 completed all measurements during dry-land swimming stroke simulations. Elbow joint angle, velocity, and acceleration were measured using inertial measurement units. The RMSE/D was calculated to determine the elbow joint ROM deviation. Joint angle (3.33  ∘ -42.96  ∘ ), angular velocity (-364.15 to 245.69  ∘ / s ), and angular acceleration (-7051.80 to 1465.35  ∘ / s 2 ) were significantly different between the critical pain and healthy. The probability distributions of joint angle (15.47  ∘ ±14.54  ∘ ), angular velocity (2.41  ∘ ±111.06  ∘ / s ), and angular acceleration (1.93 ± 2222.6  ∘ / s 2 ) in the slight pain group were significantly different betweenhealthy and critical pain. The RMSE/D distributions of angular velocity (28.3%) and acceleration (21.48%) in the critical pain deviated from the healthy. The peak value-RMSE/D matrix model obtained proved that elbow ROM significantly differed between the elite freestyle swimmers with EP and the healthy. Angular velocity and acceleration indicate the weakness and negative influence of kinematics on patients with EP. Thus, Potential solutions are to constantly optimise freestyle swimming techniques and strengthen the arm muscles.

Upper limb movement quality measures: comparing IMUs and optical motion capture in stroke patients performing a drinking task

Introduction

Clinical assessment of upper limb sensorimotor function post-stroke is often constrained by low sensitivity and limited information on movement quality. To address this gap, recent studies proposed a standardized instrumented drinking task, as a representative daily activity combining different components of functional arm use. Although kinematic movement quality measures for this task are well-established, and optical motion capture (OMC) has proven effective in their measurement, its clinical application remains limited. Inertial Measurement Units (IMUs) emerge as a promising low-cost and user-friendly alternative, yet their validity and clinical relevance compared to the gold standard OMC need investigation.

Method

In this study, we conducted a measurement system comparison between IMUs and OMC, analyzing 15 established movement quality measures in 15 mild and moderate stroke patients performing the drinking task, using five IMUs placed on each wrist, upper arm, and trunk.

Results

Our findings revealed strong agreement between the systems, with 12 out of 15 measures demonstrating clinical applicability, evidenced by Limits of Agreement (LoA) below the Minimum Clinically Important Differences (MCID) for each measure.

Discussion

These results are promising, suggesting the clinical applicability of IMUs in quantifying movement quality for mildly and moderately impaired stroke patients performing the drinking task.

Evaluating the Accuracy of Upper Limb Movement in the Sagittal Plane among Computer Users during the COVID-19 Pandemic

(1) Background: The most common musculoskeletal pathology among healthcare professionals is neck and/or shoulder pain. The aim of this study was to determine the dominant upper limb functionality concerning the ability to replicate a given movement pattern among employees reporting neck or upper limb pain while using a computer during the COVID-19 pandemic. (2) Methods: The study was conducted from March to April 2021 on a group of 45 medical employees who used a computer workstation for 4 to 6 h of their working time. In the design of this study, three study groups were created: a group of patients with pain syndrome of segment C5/C7 of the spine, a group of patients with shoulder pain syndrome, and a control group of healthy volunteers. (3) Results: The examined groups significantly differed in the correctness of performing the given movement (p = 0.001) and the minimum value of inclination during the exercise session (p = 0.026), as well as the maximum lowering (p = 0.03) in relation to the control group. (4) Conclusions: The VECTIS device can be used to assess the accuracy of reflecting the prescribed movement of the upper limb in rehabilitation programs for patients with cervical spine pain syndrome and shoulder pain syndrome.

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.

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.

A guide to inter-joint coordination characterization for discrete movements: a comparative study

Characterizing human movement is essential for understanding movement disorders, evaluating progress in rehabilitation, or even analyzing how a person adapts to the use of assistive devices. Thanks to the improvement of motion capture technology, recording human movement has become increasingly accessible and easier to conduct. Over the last few years, multiple methods have been proposed for characterizing inter-joint coordination. Despite this, there is no real consensus regarding how these different inter-joint coordination metrics should be applied when analyzing the coordination of discrete movement from kinematic data. In this work, we consider 12 coordination metrics identified from the literature and apply them to a simulated dataset based on reaching movements using two degrees of freedom. Each metric is evaluated according to eight criteria based on current understanding of human motor control physiology, i.e, each metric is graded on how well it fulfills each of these criteria. This comparative analysis highlights that no single inter-joint coordination metric can be considered as ideal. Depending on the movement characteristics that one seeks to understand, one or several metrics among those reviewed here may be pertinent in data analysis. We propose four main factors when choosing a metric (or a group of metrics): the importance of temporal vs. spatial coordination, the need for result explainability, the size of the dataset, and the computational resources. As a result, this study shows that extracting the relevant characteristics of inter-joint coordination is a scientific challenge and requires a methodical choice. As this preliminary study is conducted on a limited dataset, a more comprehensive analysis, introducing more variability, could be complementary to these results.

Assessing Spatiotemporal and Quality Alterations in Paretic Upper Limb Movements after Stroke in Routine Care: Proposal and Validation of a Protocol Using IMUs versus MoCap

Accurate assessment of upper-limb movement alterations is a key component of post-stroke follow-up. Motion capture (MoCap) is the gold standard for assessment even in clinical conditions, but it requires a laboratory setting with a relatively complex implementation. Alternatively, inertial measurement units (IMUs) are the subject of growing interest, but their accuracy remains to be challenged. This study aims to assess the minimal detectable change (MDC) between spatiotemporal and quality variables obtained from these IMUs and MoCap, based on a specific protocol of IMU calibration and measurement and on data processing using the dead reckoning method. We also studied the influence of each data processing step on the level of between-system MDC. Fifteen post-stroke hemiparetic subjects performed reach or grasp tasks. The MDC for the movement time, index of curvature, smoothness (studied through the number of submovements), and trunk contribution was equal to 10.83%, 3.62%, 39.62%, and 25.11%, respectively. All calibration and data processing steps played a significant role in increasing the agreement. The between-system MDC values were found to be lower or comparable to the between-session MDC values obtained with MoCap, meaning that our results provide strong evidence that using IMUs with the proposed calibration and processing steps can successfully and accurately assess upper-limb movement alterations after stroke in clinical routine care conditions.

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.

Data-Driven Quantitation of Movement Abnormality after Stroke

Stroke commonly affects the ability of the upper extremities (UEs) to move normally. In clinical settings, identifying and measuring movement abnormality is challenging due to the imprecision and impracticality of available assessments. These challenges interfere with therapeutic tracking, communication, and treatment. We thus sought to develop an approach that blends precision and pragmatism, combining high-dimensional motion capture with out-of-distribution (OOD) detection. We used an array of wearable inertial measurement units to capture upper body motion in healthy and chronic stroke subjects performing a semi-structured, unconstrained 3D tabletop task. After data were labeled by human coders, we trained two deep learning models exclusively on healthy subject data to classify elemental movements (functional primitives). We tested these healthy subject-trained models on previously unseen healthy and stroke motion data. We found that model confidence, indexed by prediction probabilities, was generally high for healthy test data but significantly dropped when encountering OOD stroke data. Prediction probabilities worsened with more severe motor impairment categories and were directly correlated with individual impairment scores. Data inputs from the paretic UE, rather than trunk, most strongly influenced model confidence. We demonstrate for the first time that using OOD detection with high-dimensional motion data can reveal clinically meaningful movement abnormality in subjects with chronic stroke.

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Background

Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation.

Objective

This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation.

Methods

We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question.

Results

A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures.

Conclusions

This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.

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.

Customized Manual Muscle Testing for Post-Stroke Upper Extremity Assessment

In neuro-rehabilitation, the assessment of post-stroke patients’ motor function of damaged upper extremities (UEs) is essential. Clinicians need clear and concise assessment instruments to monitor progress recorded in intensive rehabilitation sessions. One such instrument is Manual Muscle Testing (MMT), which, in our view, requires a modified scoring model aimed at improving the assessment process of patients’ motor and functional UE status, and recording their step-by-step-progress, especially if patients undergo a short length of hospitalization (of about 10 therapy days). Hence, this paper presents a new scoring system developed by the authors. This systemresults in a more precise MMT grading scale, which has more grades and can provide a more specific muscular assessment, while offering more clarity in quantifying patients’ progress after physical therapy. A prospective study was made of 41 post-stroke patients with upper extremity (UE) impairments. To determine the validity of the assessment tool for hypothesizing, and the unidimensionality and internal consistency of the customized model, exploratory and confirmatory factor analysis (CFA) with a structural equation model (SEM), Cronbach’s Alpha, and Pearson correlation coefficients were used with Fugl−Meyer (FM) assessments, the Modified Ashworth Scale (MAS), AROM, and the Modified Rankin Scale (MRS). Considering the unidimensionality of the instrument used, we performed a linear regression to identify whether certain movements performed segmentally by the manually evaluated muscles influence the measured manual score of the whole UE. All indices suggested a good model fit, and a Cronbach’s Alpha of 0.920 suggested strong internal consistency. The Pearson correlation coefficient of the MMT-customized score with AROM was 0.857, p < 0.001; that with FMUE was 0.905, p < 0.001; that with MRS was −0.608, p = 0.010; and that with MAS was −0.677, p < 0.001. The linear regression results suggest that wrist extensors, shoulder abductors, and finger flexors can influence the manual assessment of the muscle strength of the whole UE, thereby improving post-stroke patient management. The results of our research suggest that, using the proposed scoring, MMT may be a useful tool for UE assessment in post-stroke patients.

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.

Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review

Technology-aided hand functional assessment has received considerable attention in recent years. Its applications are required to obtain objective, reliable, and sensitive methods for clinical decision making. This systematic review aims to investigate and discuss characteristics of technology-aided hand functional assessment and their applications, in terms of the adopted sensing technology, evaluation methods and purposes. Based on the shortcomings of current applications, and opportunities offered by emerging systems, this review aims to support the design and the translation to clinical practice of technology-aided hand functional assessment. To this end, a systematic literature search was led, according to recommended PRISMA guidelines, in PubMed and IEEE Xplore databases. The search yielded 208 records, resulting into 23 articles included in the study. Glove-based systems, instrumented objects and body-networked sensor systems appeared from the search, together with vision-based motion capture systems, end-effector, and exoskeleton systems. Inertial measurement unit (IMU) and force sensing resistor (FSR) resulted the sensing technologies most used for kinematic and kinetic analysis. A lack of standardization in system metrics and assessment methods emerged. Future studies that pertinently discuss the pathophysiological content and clinimetrics properties of new systems are required for leading technologies to clinical acceptance.

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.

Design, Development, and Testing of an Intelligent Wearable Robotic Exoskeleton Prototype for Upper Limb Rehabilitation

Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. The experimental result shows that the device can be suitable for use in providing support for diagnostic and rehabilitation processes of neuromotor functions, although optimizations and rigorous clinical validation are required beforehand.

A 3D-Printed Soft Fingertip Sensor for Providing Information about Normal and Shear Components of Interaction Forces

Sensing of the interaction forces at fingertips is of great value in assessment and rehabilitation therapy. Current force sensors are not compliant to the fingertip tissue and result in loss of touch sensation of the user. This work shows the development and characterization of a flexible fully-3D-printed piezoresistive shear and normal force sensor that uses the mechanical deformation of the finger tissue. Two prototypes of the sensing structure are evaluated using a finite element model and a measurement setup that applies normal and shear forces up to 10 N on a fingertip phantom placed inside the sensing structure, which is fixed to prevent slippage. Furthermore, the relation between strain (rate) and resistance of the conductive TPU, used for the strain gauges, is characterized. The applied normal and shear force components of the 3D-printed sensing structure can be partly separated. FEM analysis showed that the output of the sensor is largely related to the sensor geometry and location of the strain gauges. Furthermore, the conductive TPU that was used has a negative gauge factor for the strain range used in this study and might cause non-linear behaviors in the sensor output.

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.