Rehabilitation Engineering Approach to Frozen Shoulder Treatment: Performance Analysis Using Landmark-Based Motion Detection and Assistive Feedback Systems

Information from the abstract

This paper presents a preliminary technical feasibility study of a landmark-based motion analysis system designed for potential future application in home-based rehabilitation monitoring for frozen shoulder (adhesive capsulitis), developed using computer vision (CV) and human–computer interaction (HCI) principles. The proposed system utilizes real-time body landmark detection to quantify shoulder joint kinematics and provide rule-based automated feedback on exercise execution. The system combines automated and manual components: while shoulder angle assessment, cosine similarity analysis, and keyframe matching are automated, manual researcher input is required to define keyframes corresponding to movement states (start, midpoint, peak) for each therapeutic pose. The CV-driven perception is translated into HCI output, including quantitative movement scores and rule-based feedback indicators, demonstrating the technical potential for objective evaluation of rehabilitation exercise execution without specialized wearable sensors. Technical validation was conducted with 14 healthy volunteers (not frozen shoulder patients) executing standardized shoulder rehabilitation activities, demonstrating shoulder angle measurement with an overall mean absolute error (MAE) of 7.03° against general goniometry and 6.61° against clinical goniometry (RMSE: 8.50° and 8.79°, respectively). Movement similarity classification achieved F1-scores ranging from 0.870 (flexion) to 1.0 (internal rotation) when compared against expert evaluation, though these results are based on a controlled and largely imbalanced dataset with limited incorrect movement examples. The system additionally incorporates a facial expression recognition (FER) module, previously developed and validated in the authors’ prior work, as a supplementary component to support future integration of pain monitoring; this module was not independently validated in the present study. This preliminary technical feasibility study contributes to rehabilitation engineering by demonstrating the potential of semi-automated CV-based motion analysis and rule-based HCI feedback for shoulder movement assessment. The findings indicate technical feasibility for future investigation in home-based exercise monitoring; however, clinical utility cannot be claimed at this stage, as validation with actual frozen shoulder patient cohorts is required.

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