将生理模型与神经网络结合，提升肌电信号运动估计的准确性和可解释性。
Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation
- 融合肌肉骨骼动力学模型与残差学习，兼顾生理合理性与精度
- 在6名健康受试者上，RMSE和R²均优于现有方法
- 适合康复机器人、假肢控制等需要可解释性的场景
从表面肌电（sEMG）中准确解码人类运动意图对于肌电控制至关重要，广泛应用于康复机器人与辅助技术。然而，现有sEMG运动估计方法通常依赖难以校准的个体化肌肉骨骼（MSK）模型，或缺乏生理一致性的纯数据驱动模型。本文提出一种新型物理嵌入神经网络（PENN），将可解释的MSK前向动力学与数据驱动的残差学习相结合，既保持生理一致性，又实现高精度运动估计。PENN采用递归时序结构传播历史估计，并使用轻量卷积神经网络进行残差修正，实现鲁棒且时间连贯的估计。设计了两阶段训练策略。在六名健康受试者的实验评估中，PENN在均方根误差（RMSE）和决定系数（R²）指标上均优于现有先进方法。
原文摘要 · Abstract (English)
Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assistive technologies. However, existing sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate, or purely data-driven models that lack physiological consistency. This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning, thereby preserving physiological consistency while achieving accurate motion estimation. The PENN employs a recursive temporal structure to propagate historical estimates and a lightweight convolutional neural network for residual correction, leading to robust and temporally coherent estimations. A two-phase training strategy is designed for PENN. Experimental evaluations on six healthy subjects show that PENN outperforms state-of-the-art baseline methods in both root mean square error (RMSE) and $R^2$ metrics.
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