21.08.2026journal-article
Scientific Reports
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 ma …