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.

翻译：表面肌电图（sEMG）为肌肉功能提供了关键信息，但其信号易受噪声干扰且采集困难。惯性测量单元（IMU）为运动捕捉系统提供了一种鲁棒且可穿戴的替代方案。本文研究利用深度学习方法从六轴IMU数据合成归一化的sEMG信号。我们以1kHz采样率同步采集了多种手臂动作的sEMG与IMU数据。基于扩张因果卷积的滑动窗口波网模型被训练用于将IMU数据映射至sEMG信号。结果表明，该模型能成功预测肌肉激活的时序与整体形态。尽管峰值幅度常被低估，但其高时间保真度证明了该方法在假肢控制及康复生物反馈等应用中用于肌肉意图检测的可行性。