Trunk Compensation During Bimanual Reaching at Different Heights by Healthy and Hemiparetic Adults

Effects of Upper Limb Control on the Less-Affected Side on Upper Limb Function, Respiration, Balance, and Activities of Daily Living in Stroke

Background and Objectives: This study aimed to investigate the effects of upper limb control exercises on upper limb function, respiration, balance, and activities of daily living in patients with stroke. Materials and Methods: The 28 patients who met the selection criteria were randomly assigned to two groups of 14 patients each. Subsequently, upper limb control exercises using real-time feedback were applied. The same interventional exercise was applied to both the less-affected and affected limbs of the study participants, who were classified into a less-affected side upper limb control group and an affected side upper limb control exercise group. Interventional exercises, 30 min each, were performed five times weekly for 4 weeks, and follow-up examinations were performed 2 weeks after the end of exercise. Electronic muscle strength measurements and an electronic goniometer were used to evaluate upper limb function. A spirometer was used to measure respiration. Balance ability was evaluated using a force plate pressure distribution measuring system with a sensor that detects the movement of the body center on the ground. Daily life movements were evaluated using the Korean version of the modified Barthel index. Results: When examining the results, the upper limb function on the paralyzed side showed an increase in the electromyographic strength of shoulder joint depression and flexion angle. Improvements were also observed in respiration (forced vital capacity [L] and forced expiratory volume in 1 s [L]), balance (95% confidence ellipse area [mm2] and center of pressure displacement [mm]), and daily life activities, all of which showed statistically significant differences in the time × group interaction effect (p < 0.05). Conclusions: Thus, it was found that the upper limb control exercise on the less-affected side had a significant effect when the exercise was performed together with treatment on the affected side in patients with stroke. It is anticipated that this study will provide basic data for evaluating both the trunk and upper limbs of the less-affected and affected sides.

Development of a compensation-aware virtual rehabilitation system for upper extremity rehabilitation in community-dwelling older adults with stroke

BACKGROUND

Compensatory movements are commonly observed in older adults with stroke during upper extremity (UE) motor rehabilitation, which could limit their motor recovery.

AIM

This study aims to develop a compensation-aware virtual rehabilitation system (VRS) that can detect compensatory movements and improve the outcome of UE rehabilitation in community-dwelling older adults with stroke.

METHODS

The VRS development includes three main components: (1) the use of thresholds for determining compensatory movements, (2) the algorithm for processing the kinematic data stream from Kinect to detect compensation in real-time, and (3) the audio-visual feedback to assist older adults with stroke to be aware of the compensation. Two studies were conducted following the VRS development, where Study 1 identified the value of thresholds for determining compensatory movements in two planar motor exercises, and Study 2 provided preliminary validation for the developed VRS by comparing two groups undergoing VR training or conventional training (CT) in a community rehabilitation center.

RESULTS

The VRS could effectively detect all determined compensatory movements and timely trigger feedback in response to the detected compensatory movements. The VR participants showed significant improvements in Fugl-Meyer Assessment-Upper Extremity (FMA-UE, p = 0.045) and Wolf Motor Function Test (WMFT, p = 0.009). However, the VR and CT groups had no significant differences in outcome measures.

CONCLUSION

The VRS demonstrates the ability to detect compensation and the potential of assisting older adults with stroke to improve motor functions. Suggestions are given for further improvements of the VRS to support the older adult with stroke to reduce compensation.

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.

The effects of an object's height and weight on force calibration and kinematics when post-stroke and healthy individuals reach and grasp

Impairment in force regulation and motor control impedes the independence of individuals with stroke by limiting their ability to perform daily activities. There is, at present, incomplete information about how individuals with stroke regulate the application of force and control their movement when reaching, grasping, and lifting objects of different weights, located at different heights. In this study, we assess force regulation and kinematics when reaching, grasping, and lifting a cup of two different weights (empty and full), located at three different heights, in a total of 46 participants: 30 sub-acute stroke participants, and 16 healthy individuals. We found that the height of the reached target affects both force calibration and kinematics, while its weight affects only the force calibration when post-stroke and healthy individuals perform a reach-to-grasp task. There was no difference between the two groups in the mean and peak force values. The individuals with stroke had slower, jerkier, less efficient, and more variable movements compared to the control group. This difference was more pronounced with increasing stroke severity. With increasing stroke severity, post-stroke individuals demonstrated altered anticipation and preparation for lifting, which was evident for either cortical lesion side.

The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching

Compensatory movements at the trunk are commonly utilized during reaching by persons with motor impairments due to neurological injury such as stroke. Recent low-cost motion sensors may be able to measure trunk compensation, but their validity and reliability for this application are unknown. The purpose of this study was to compare the first (K1) and second (K2) generations of the Microsoft Kinect to a video motion capture system (VMC) for measuring trunk compensation during reaching. Healthy participants (n = 5) performed reaching movements designed to simulate trunk compensation in three different directions and on two different days while being measured by all three sensors simultaneously. Kinematic variables related to reaching range of motion (ROM), planar reach distance, trunk flexion and lateral flexion, shoulder flexion and lateral flexion, and elbow flexion were calculated. Validity and reliability were analyzed using repeated-measures ANOVA, paired t-tests, Pearson’s correlations, and Bland-Altman limits of agreement. Results show that the K2 was closer in magnitude to the VMC, more valid, and more reliable for measuring trunk flexion and lateral flexion during extended reaches than the K1. Both sensors were highly valid and reliable for reaching ROM, planar reach distance, and elbow flexion for all conditions. Results for shoulder flexion and abduction were mixed. The K2 was more valid and reliable for measuring trunk compensation during reaching and therefore might be prioritized for future development applications. Future analyses should include a more heterogeneous clinical population such as persons with chronic hemiparetic stroke.

Robotics-assisted visual-motor training influences arm position sense in three-dimensional space

Background

Performing activities of daily living depends, among other factors, on awareness of the position and movements of limbs. Neural injuries, such as stroke, might negatively affect such an awareness and, consequently, lead to degrading the quality of life and lengthening the motor recovery process. With the goal of improving the sense of hand position in three-dimensional (3D) space, we investigate the effects of integrating a pertinent training component within a robotic reaching task.

Methods

In the proof-of-concept study presented in this paper, 12 healthy participants, during a single session, used their dominant hand to attempt reaching without vision to two targets in 3D space, which were placed at locations that resembled the functional task of self-feeding. After each attempt, participants received visual and haptic feedback about their hand’s position to accurately locate the target. Performance was evaluated at the beginning and end of each session during an assessment in which participants reached without visual nor haptic feedback to three targets: the same two targets employed during the training phase and an additional one to evaluate the generalization of training.

Results

Collected data showed a statistically significant [39.81% (p=0.001)] reduction of end-position reaching error when results of reaching to all targets were combined. End-position error to the generalization target, although not statistically significant, was reduced by 15.47%.

Conclusions

These results provide support for the effectiveness of combining an arm position sense training component with functional motor tasks, which could be implemented in the design of future robot-assisted rehabilitation paradigms to potentially expedite the recovery process of individuals with neurological injuries.

Visual vs vibrotactile feedback for posture assessment during upper-limb robot-aided rehabilitation

Repetitive and intensive exercises during robot-aided rehabilitation may expose patients to inappropriate and unsafe postures. The introduction of a sensory feedback can help the subject to perform the rehabilitation task with an ergonomic posture. In this work, the introduction of visual and vibrotactile feedback in a robotic platform for upper limb rehabilitation has been proposed to ensure ergonomic posture during rehabilitation. The two feedback modalities have been used to provide information about incorrect neck and trunk posture. Ten healthy subjects have been involved in this study. Each of them performed 3D reaching movements with the aid of the robotic platform in three different conditions, i.e. without feedback, with visual feedback and with vibrotactile feedback, and a comparative analysis has been carried out to evaluate feedback effectiveness, acceptance and performance. Experimental results show that in case of no feedback the subjects reach and maintain configurations that can lead to incorrect neck and trunk configurations and therefore, if repeated, to musculoskeletal disorders. Conversely, with visual or vibrotactile feedback, the subjects tend to correct inappropriate posture with both trunk and head during task performing.

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy

OBJECTIVES

Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the real-time monitoring of compensation in patients with stroke by using pressure distribution data and machine learning algorithms. Whether trunk compensation can be reduced by combining the online detection of compensation and haptic feedback of a rehabilitation robot was also investigated.

METHODS

Six patients with stroke did three forms of reaching movements while pressure distribution data were recorded as Dataset1. A support vector machine (SVM) classifier was trained with features extracted from Dataset1. Then, two other patients with stroke performed reaching tasks, and the SVM classifier trained by Dataset1 was employed to classify the compensatory patterns online. Based on the real-time monitoring of compensation, a rehabilitation robot provided an assistive force to patients with stroke to reduce compensations.

RESULTS

Good classification performance (F1 score > 0.95) was obtained in both offline and online compensation analysis using the SVM classifier and pressure distribution data of patients with stroke. Based on the real-time detection of compensatory patterns, the angles of trunk rotation, trunk lean-forward and trunk-scapula elevation decreased by 46.95%, 32.35% and 23.75%, respectively.

CONCLUSION

High classification accuracies verified the feasibility of detecting compensation in patients with stroke based on pressure distribution data. Since the validity and reliability of the online detection of compensation has been verified, this classifier can be incorporated into a rehabilitation robot to reduce trunk compensations in patients with stroke.

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.

Dynamics of post-stroke hand paresis kinematic pattern during rehabilitation

According to the literature data, only 5–20% of post-stroke patients are able to restore the hand motor function completely. Correct goal setting and individual approach to the patient's functional recovery are important. Our study aimed to develop an algorithm of impaired hand motor functioning assessment for post-stroke patients and to determine the principles of the rehabilitation tactics choosing based on the biomechanical analysis. Twenty five patients with hemispheric stroke and 10 healthy volunteers participated in the study. Formal clinical observation scales (Fugl-Meyer Assessment, Ashworth Scale, ARAT) and video motion analysis were used for evaluation of the hand motor function. Patients were divided into 2 groups according to the hand paresis severity (mild/moderate and pronounced/severe). Rehabilitation was carried out in both groups, including mechanotherapy, massage and physical therapy. It was revealed that in the 1st group of patients the motor function recovery in the paretic hand was due to movement performance recovery: biomechanical parameters restoration directly correlated with a decrease in the paresis degree according to the Fugl-Meyer Assessment Scale (r = 0.94; p = 0.01). In the 2nd group of patients, the motor function recovery in the paretic hand was due to motor deficit compensation: according to biomechanical analysis, the pathological motor synergies inversely correlated with a decrease in the paresis degree (r = –0.9; p = 0.03). As a result of the study, an algorithm for selecting the patient management tactics based on the baseline clinical indicators was developed.

Biofeedback vs. game scores for reducing trunk compensation after stroke: a randomized crossover trial

Background Compensatory movements are commonly employed by stroke survivors, and their use can have negative effects on motor recovery. Current practices to reduce them rely on strapping a person to a chair. The use of technology to substitute or supplement this methodology has not being thoroughly investigated. Objective To compare the use of Scores + Visual + Force and Visual + Force feedback for reducing trunk compensation. Methods Fourteen hemiparetic stroke survivors performed bimanual reaching movements while receiving feedback on trunk compensation. Participants held onto two robotic arms and performed movements in the anterior/posterior direction toward a target displayed on a monitor. A motion-tracking camera tracked trunk compensation; the robots provided force feedback; the monitor displayed the visual feedback and scores. Kinematic variables, a post-test questionnaire, and system usability were analyzed. Results Both conditions reduced trunk compensation from baseline: Scores + Visual + Force: 51.7% (40.8), p = 0.000; Visual + Force: 55.2% (40.9), p = 0.000. No statistically significant difference was found between modalities. Secondary outcome measures were not improved. Most participants would like to receive game scores to reduce trunk compensation, and the usability of the system was rated "Good." Conclusions Multimodal feedback about stroke survivors' trunk compensation levels resulted in reduced trunk displacement. No difference between feedback modalities was obtained. The positive effects of including game scores might not have been observed in a short-term intervention. Longer studies should investigate if the use of game scores could result in trunk compensation improvements when compared to trunk restraint strategies. Clinical Trial Registration Clinicaltrials.gov, NCT02912923, https://clinicaltrials.gov/ct2/show/NCT02912923?term=reaching+in+stroke&rank=2 .