摘要
针对双臂协同连续变化下肌力估计精度低的问题,提出一种双向长短期记忆(BiLSTM)网络与自注意力(SA)机制相结合的肌力估计模型。首先,通过搭建肌力估计试验平台采集双臂肌肉等长收缩状态下的肌力与表面肌电信号,然后采用独立成分分析方法以及小波阈值去噪方法对采集数据进行预处理,提取信号的均方根作为特征值,最后利用BiLSTM-SA模型进行肌力估计。实验结果表明BiLSTM-SA模型在双臂等长收缩肌力估计中决定系数R2的平均值在0.97以上,表现出良好的肌力估计准确性。
A muscle strength estimation model incorporating bidirectional long short-term memory network(BiLSTM)and self-attention mechanism(SA)is presented for addressing the problem of low estimation accuracy caused by the continuous change of arms.After collecting the muscle strength and surface electromyography signal during isometric muscle contraction of arms with a self-developed platform,independent component analysis and wavelet threshold denoising are used to preprocess the collected signals.With the root-mean-square value of the extracted signals as the characteristic value,the muscle strength is estimated using BiLSTM-SA model.The experimental results show that BiLSTM-SA model has a high accuracy for muscle strength estimation,with an average value of R2 above 0.97 for the muscle strength estimation during isometric contraction.
作者
张思河
曹乐
王金玮
徐浩洋
张峰
ZHANG Sihe;CAO Le;WANG Jinwei;XU Haoyang;ZHANG Feng(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《中国医学物理学杂志》
CSCD
2023年第11期1383-1389,共7页
Chinese Journal of Medical Physics
基金
国家自然科学基金(61703270)。
关键词
双向长短期记忆网络
自注意力机制
表面肌电信号
独立成分分析
bidirectional long short-term memory network
self-attention mechanism
surface electromyography signal
independent component analysis
