基于HD-sEMG的等速收缩过程中腕关节角度和扭矩的同时估计。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Mingjie Yan;Zhe Chen;Jianmin Li;Jinhua Li;Lizhi Pan
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引用次数: 0

摘要

建立自然流畅的人机界面是实现肌电控制的关键,这就需要一种有效的动作意图解码方法。本研究基于高密度表面肌电图(HD-sEMG),探索了一种在等速收缩过程中同时估计手腕关节角度和扭矩的方法。10名健全人被要求完成不同运动模式的腕关节等速屈伸任务,并收集其HD-sEMG信号。为了对这些信号进行解码,我们建立了一个包含全局注意机制的卷积神经网络(CNN),命名为全局注意卷积神经网络(GACNN)。此外,还使用了支持向量机(SVM)、残差网络(ResNet)、长短期记忆(LSTM)、基于变压器的模型(TBM)、基于肌肉协同的图注意网络(MSGAT-LSTM)和时空特征提取网络(STFEN)等6种解码模型对腕部关节角度和扭矩进行连续估计。采用归一化均方根误差(NRMSE)和Pearson相关系数(PCC)等评价指标评价7个模型的估计性能。GACNN的估计性能明显优于SVM、LSTM、ResNet、STFEN,在某些估计情况下也优于TBM和MSGAT-LSTM。GACNN的NRMSE和PCC平均值分别为0.080±0.013和0.955±0.016。研究结果显示了结合全局注意机制的神经网络的优越性,对人机交互的应用具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG
The establishment of a natural and smooth human-computer interface is crucial for myoelectric control, which requires an effective decoding method for movement intention. Based on high-density surface electromyography (HD-sEMG), this study explored a method to simultaneously estimate wrist joint angle and torque during isokinetic contraction. Ten able-bodied individuals were instructed to complete wrist isokinetic flexion and extension tasks with different movement patterns, and the HD-sEMG signals were collected. To decode these signals, a convolutional neural network (CNN) incorporating the global attention mechanism was established, named global attention convolutional neural network (GACNN). Six other decoding models were also used to continuously estimate the wrist joint angle and torque, including support vector machine (SVM), residual network (ResNet), long short-term memory (LSTM), transformer-based model (TBM), muscle synergy-based graph attention networks (MSGAT-LSTM), and spatio-temporal feature extraction network (STFEN). Evaluation metrics including normalized root mean square error (NRMSE) and Pearson’s correlation coefficient (PCC) were applied to evaluate the estimation performance of the seven models. The GACNN showed significantly better estimation performance than SVM, LSTM, ResNet, STFEN and it also demonstrated superior performance over TBM and MSGAT-LSTM in some estimation cases. On average, for all subjects, NRMSE and PCC of the GACNN were $0.080~\pm ~0.013$ and $0.955~\pm ~0.016$ . The result shows the superiority of the neural network incorporating global attention mechanism, which is of great significance for the application of human-computer interaction.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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