Camera-Based Home Rehabilitation Exercise Monitoring: A Technical Evaluation Against Optical Motion Capture

Abstract

Home-based rehabilitation often relies on patients performing prescribed exercises independently, without continuous supervision from a physiotherapist. A digital solution that can monitor the exercise performance and provide feedback to the patients would be valuable to improve the follow-ups and support patient empowerment. Camera-based markerless pose estimation may provide a practical and lowcost way to monitor exercise quality in such settings. This thesis investigates the feasibility of using a single RGB camera and MediaPipe Pose for selected rehabilitationoriented exercises. Three exercises were evaluated: single-leg stance, sit-to-stand, and mini-squat. RGB videos were recorded using an iPhone 13, while reference motion data were collected with a Qualisys optical motion capture system. MediaPipe Pose was used to extract body landmarks from the videos, and exercise-specific metrics were computed, including trunk orientation, normalized pelvis displacement, squat depth, movement timing, and knee flexion. To enable comparison with MediaPipe, the three-dimensional motion-capture data were projected onto corresponding frontal or sagittal analysis planes before matched metric definitions were applied. The results show that the MediaPipe-based pipeline could estimate several selected metrics with small to moderate errors under controlled condition 1 setup which is camera-to-participant at 3 m with normal room lighting. Trunk-orientation and normalized displacement metrics were generally more stable than two-dimensional knee-flexion estimation. The results also showed that performance depended on the exercise, metric definition, participant, and recording setup. Camera distance and lighting affected the metrics differently, and repeatability was generally stronger within the same session than across different days. These findings indicate that MediaPipe-based rehabilitation monitoring should be interpreted at the metric level rather than as a uniformly accurate motion-analysis solution. A rule-based feedback prototype was also developed to illustrate how pose-derived metrics could be translated into patient-facing feedback and therapist-facing session review outputs. Overall, the findings suggest that MediaPipe Pose can be a useful low-cost component for selected home rehabilitation monitoring tasks, provided that the exercise, camera view, and metric definitions are carefully chosen. Further validation with larger and more realistic datasets is required before clinical or real home deployment.