ArticleIEEE journal of translational engineering in health and medicine2018
Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy.
Article in IEEE journal of translational engineering in health and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Motor Impairment and Adaptation in a Novel Nonhuman Primate Model of Internal Capsule Infarct.Stroke · 2026Article
- Smartphone-Based Markerless Motion Capture for Accessible Rehabilitation: A Computer Vision Study.Sensors (Basel, Switzerland) · 2025Article
- Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises.Scientific reports · 2025Article
- Survey on Context-Aware Radio Frequency-Based Sensing.Sensors (Basel, Switzerland) · 2025Review
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Online detection of compensatory strategies in human movement with supervised classification: a pilot study.Frontiers in neurorobotics · 2023Article
- Compensatory movement detection by using near-infrared spectroscopy technology based on signal improvement method.Frontiers in neuroscience · 2023Article
- Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors.Journal of neuroengineering and rehabilitation · 2020Article
- Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots.Frontiers in robotics and AI · 2020Article
- Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms.Journal of neuroengineering and rehabilitation · 2019Article
- sEMG-Based Trunk Compensation Detection in Rehabilitation Training.Frontiers in neuroscience · 2019Article
- Article
- Review
- Considerations for at-home upper-limb rehabilitation technology following stroke: Perspectives of stroke survivors and therapists.Journal of rehabilitation and assistive technologies engineeringArticle
- Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.