Cai Zixiang, Qu Mengyao, Han Mingyang, Wu Zhijing, Wu Tong, Liu Mengtong, Yu Hailong
School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2024 Dec 24;25(1):13. doi: 10.3390/s25010013.
This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality.
本研究旨在利用表面肌电图（sEMG）信号预测和拟合人体上肢的非线性动态握力。该研究采用基于时间序列的神经网络NARX，建立前臂肌肉群的肌电信号与动态握力之间的映射关系。使用三通道肌电信号采集设备和握力传感器记录受试者在特定动态力条件下的肌肉信号和握力数据。在对数据进行预处理（包括去除异常值、小波去噪和基线漂移校正）后，使用NARX模型进行拟合分析。该模型比较了两种不同的训练策略：正则化随机梯度下降（BRSGD）和共轭梯度（CG）。结果表明，CG大大缩短了训练时间，且性能没有下降。NARX在动态握力预测中表现出良好的准确性和稳定性，其中具有10层和20个时间延迟的模型表现最佳。结果表明，所提出的方法在智能假肢和虚拟现实的力控制应用中具有潜在的实际意义。
