用惯性传感器数据重建肌电信号，助力假肢与康复反馈
Reconstruction of Surface EMG Signal using IMU data for Upper Limb Actions
- 用6轴惯性传感器数据，通过深度网络预测肌电信号
- 时间对齐准确，能捕捉肌肉激活的时序特征
- 适合假肢控制和康复训练中的意图识别场景
表面肌电（sEMG）可提供肌肉功能的重要信息，但易受噪声干扰且采集困难。惯性测量单元（IMU）则是一种鲁棒、可穿戴的运动捕捉替代方案。本文研究基于深度学习方法，从6轴IMU数据合成归一化sEMG信号。实验采集了多种上肢动作下同步的sEMG与IMU数据，采样频率为1 kHz。采用基于膨胀因果卷积的滑动窗口波网络（Sliding-Window-Wave-Net）模型，将IMU数据映射至sEMG信号。结果表明，模型能够准确预测肌肉激活的时间点和整体波形形状。尽管峰值幅度常被低估，但高时间保真度验证了该方法在假肢控制与康复生物反馈等应用中进行肌肉意图检测的可行性。
原文摘要 · Abstract (English)
Surface Electromyography (sEMG) provides vital insights into muscle function, but it can be noisy and challenging to acquire. Inertial Measurement Units (IMUs) provide a robust and wearable alternative to motion capture systems. This paper investigates the synthesis of normalized sEMG signals from 6-axis IMU data using a deep learning approach. We collected simultaneous sEMG and IMU data sampled at 1~KHz for various arm movements. A Sliding-Window-Wave-Net model, based on dilated causal convolutions, was trained to map the IMU data to the sEMG signal. The results show that the model successfully predicts the timing and general shape of muscle activations. Although peak amplitudes were often underestimated, the high temporal fidelity demonstrates the feasibility of using this method for muscle intent detection in applications such as prosthetics and rehabilitation biofeedback.
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