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

Abstract

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.

Multimodal Sensing in Stroke Motor Rehabilitation

“…Determining future research directions related to gait biofeedback in context of evolving technologies [15] The application of VR in motor rehabilitation after stroke 1) The technologies used in VR rehabilitation, including sensors 2) The clinical application of and evidence for VR in stroke rehabilitation 3) Considerations for VR application in stroke rehabilitation. [16] Discussing the shortcomings of various sensors in compensation evaluation and detection Explore how technology-based methods were used to assess and detect compensation without the constant care of therapists [17] Aggregating both quantitative and qualitative knowledge from clinical research with wearable sensor technology in individuals with epilepsy, PD, and stroke Summarizing clinical application areas for all three diseases, key functions and clinical attributes measured by wearables, the proportion of reported missing data, compliance, and perceived experiences and preferences of wearables. [18] Reviewing patient demographics, the type of wearable technology used, gait parameter assessments, and measures of reliability and validity…”

Surface-Electromyography-Based Co-Contraction Index for Monitoring Upper Limb Improvements in Post-Stroke Rehabilitation: A Pilot Randomized Controlled Trial Secondary Analysis

“…As planar robots constitute a standard paradigm for upper limb rehabilitation, the design of these devices should be modified to achieve a greater involvement of the distal segment, or additional exercises of the distal region should be administered in case of using planar robots. The excessive shoulder cocontraction following conventional therapy could be related to a less precise and repeatable, and therefore less effective, therapeutic effect [40] compared to the robotic therapy, on the well-known problems of shoulder impairments after stroke both in terms of glenohumeral joint injury [41] and overuse in the adopted compensatory strategy [42].…”

Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study

“…Moreover, compensatory actions, such as elbow flexion, are frequently observed during hemiplegic shoulder flexion [ 20 ], and the excessive use of compensatory actions leads to a reduced range of joint motion, pain, and limited functional improvement in the hemiplegic upper-extremity [ 21 ]. Therefore, abnormal movements during shoulder elevation should be minimized.…”