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<p>Accurate and interpretable evaluation of rehabilitation motion quality remains challenging due to the subjectivity of manual scoring and the latency of existing sensor-based systems. This paper presents RehabNet, a lightweight and interpretable deep framework designed for real-time rehabilitation motion assessment using wearable inertial measurement units (IMUs). RehabNet integrates a Stage-Aware Multi-scale Attention (SAMA) module with temporal convolutional and recurrent encoders to capture both short- and long-term motion dependencies. The proposed SAMA mechanism adaptively highlights phase-specific motion cues, improving interpretability and robustness across diverse exercises. Extensive experiments on rehabilitation datasets demonstrate that RehabNet achieves 94.3% accuracy, MSE of 0.027, and inference latency under 10 ms, outperforming existing methods in both precision and efficiency. Furthermore, visual attention heatmaps reveal interpretable focus on critical movement phases, supporting clinical decision-making. These results indicate that RehabNet offers a practical and deployable solution for real-time, resource-efficient, and interpretable rehabilitation monitoring.</p>

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A lightweight real-time framework for interpretable rehabilitation motion quality assessment using wearable inertial sensors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Zhen Liu,
  • Quanrong Fang

摘要

Accurate and interpretable evaluation of rehabilitation motion quality remains challenging due to the subjectivity of manual scoring and the latency of existing sensor-based systems. This paper presents RehabNet, a lightweight and interpretable deep framework designed for real-time rehabilitation motion assessment using wearable inertial measurement units (IMUs). RehabNet integrates a Stage-Aware Multi-scale Attention (SAMA) module with temporal convolutional and recurrent encoders to capture both short- and long-term motion dependencies. The proposed SAMA mechanism adaptively highlights phase-specific motion cues, improving interpretability and robustness across diverse exercises. Extensive experiments on rehabilitation datasets demonstrate that RehabNet achieves 94.3% accuracy, MSE of 0.027, and inference latency under 10 ms, outperforming existing methods in both precision and efficiency. Furthermore, visual attention heatmaps reveal interpretable focus on critical movement phases, supporting clinical decision-making. These results indicate that RehabNet offers a practical and deployable solution for real-time, resource-efficient, and interpretable rehabilitation monitoring.