计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 75-78.doi: 10.11896/JsJkx.190900143
宋岩, 胡瑢华, 郭福民, 袁新亮, 熊睿洋
SONG Yan, HU Rong-hua, GUO Fu-min, YUAN Xin-liang and XIONG Rui-yang
摘要： 康复训练过程中患者需要外部设备的辅助才能完成运动。在此过程中,患者的肌肉功能逐渐康复,辅助设备所提供的辅助力逐渐变小,这要求康复训练设备能够对较大范围肌力做出准确预测。针对这一问题,提出优化一种基于表面肌电信号(Surface Electromyography,sEMG)的分层算法来精确预测肌力大小。第一层算法采用粒子群优化(Particle Swarm Optimization,PSO)算法对支持向量机算法(Support Vector Machines,SVM)进行改进,以解决sEMG中含有噪声的问题和信号本身的非线性可分问题,并使用改进后的SVM构建3分类器,对肌力大小进行高、中、低3个类别的初步划分。第二层算法采用3个对应于不同肌力大小的BP神经网络对肌力进行精准预测。由实验得出结果:20次重复计算得到的平均绝对误差为0.58,方差为0.18。因此,使用PSO_SVM+BP的组合模型方案能够满足肌力预测的精度要求。
中图分类号:
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