Webcam-Based Exergame for Motor Recovery with Physical Assessment via DTW

This paper presents RehabHub, a home-based exergaming system that integrates standardized physical assessment directly into gameplay by using a common webcam and MediaPipe for real-time pose estimation. The system quantifies upper-limb movement quality, specifically abduction, shoulder flexion, and elbow flexion based on FMA-UE guidelines, by applying Dynamic Time Warping (DTW) together with a Z-score-based scoring model that relies on data from non-clinical adult participants. A pilot study, which included movements simulated with a 5-kg resistance band, evaluated three feature-extraction methods. The findings indicate that the single-angle method provides the clearest distinction between normal and abnormal movements, particularly for abduction and elbow flexion. In the case of shoulder flexion, the score separation was less distinct because of movement variability and posture-related angle fluctuations, which suggests that further refinement of feature design is needed. The cloud-based platform supports remote monitoring and gives caregivers access to both performance scores and recorded exercise videos. Overall, the results demonstrate the feasibility of a low-cost webcam-based assessment integrated into exergaming, and they highlight important trends for improving abnormal-movement detection in home rehabilitation systems.

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Publication Details

Journal
Sensors
Published
2026-02-13
DOI
https://doi.org/10.3390/s26041219
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
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article

Webcam-Based Exergame for Motor Recovery with Physical Assessment via DTW

Worawat Choensawat, Pujana Paliyawan, Researcher Kingkarn Sookhanaphibarn, Norapat Labchurat
Sensors
Stroke Rehabilitation and Recovery
article

Webcam-Based Exergame for Motor Recovery with Physical Assessment via DTW

Worawat Choensawat, Pujana Paliyawan, Researcher Kingkarn Sookhanaphibarn, Norapat Labchurat
article en

Abstract

This paper presents RehabHub, a home-based exergaming system that integrates standardized physical assessment directly into gameplay by using a common webcam and MediaPipe for real-time pose estimation. The system quantifies upper-limb movement quality, specifically abduction, shoulder flexion, and elbow flexion based on FMA-UE guidelines, by applying Dynamic Time Warping (DTW) together with a Z-score-based scoring model that relies on data from non-clinical adult participants. A pilot study, which included movements simulated with a 5-kg resistance band, evaluated three feature-extraction methods. The findings indicate that the single-angle method provides the clearest distinction between normal and abnormal movements, particularly for abduction and elbow flexion. In the case of shoulder flexion, the score separation was less distinct because of movement variability and posture-related angle fluctuations, which suggests that further refinement of feature design is needed. The cloud-based platform supports remote monitoring and gives caregivers access to both performance scores and recorded exercise videos. Overall, the results demonstrate the feasibility of a low-cost webcam-based assessment integrated into exergaming, and they highlight important trends for improving abnormal-movement detection in home rehabilitation systems.

SensorsVol. 26(4)
Ritsumeikan University (JP), Bangkok University (TH)
Good health and well-being
Openalex Percentile: Top 56%
Stroke Rehabilitation and Recovery
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