Interpretable rehabilitation-oriented motion anomaly detection using wearable sensor data

Abstract Accurate detection of motion anomalies during physical rehabilitation is important for patient safety and recovery monitoring. Traditional assessment methods rely on visual observation. This often leads to subjective and inconsistent evaluations. To address this limitation, we propose a Support Vector-Guided Decomposition (SVGD) framework for semi-supervised rehabilitation-oriented motion anomaly detection using wearable sensor data. The framework integrates low-rank and sparse matrix decomposition with margin-based discriminative learning. Multi-sensor motion signals are processed through sequential layers of preprocessing, feature extraction, decomposition, and anomaly scoring. This design enables separation of structured rehabilitation-like movements from irregular deviations and compensatory behaviors. The framework was evaluated using structured rehabilitation-like motion data with controlled perturbation-based anomaly generation under the Leave-One-Subject-Out (LOSO) validation protocol. This provides a systematic proxy evaluation in the absence of clinically annotated rehabilitation datasets. The proposed approach achieves strong performance, with a Precision of 0.97, Recall of 0.96, F1-score of 0.965, and AUC of 0.98. These results outperform both classical and deep learning baselines. The findings demonstrate that SVGD is robust, interpretable, and computationally efficient. The framework performs well even with limited labeled data. These results highlight the potential of SVGD for rehabilitation-oriented motion anomaly detection using wearable sensor data. Validation on clinically annotated rehabilitation datasets will be considered in future work.