自动化学报2017，Vol.43Issue(3)：430-438,9.DOI:10.16383/j.aas.2017.c160114
基于有监督Kohonen神经网络的步态识别
Gait Recognition Based on Supervised Kohonen Neural Network
摘要
Abstract
Surface electromyography (sEMG) is changeable with time, which will affect the classification accuracy. The traditional recognition method cannot guarantee its effectiveness within whole control cycle for lower limb movement. This paper extracts the feature from initial 200 ms EMG, applies Kohonen and supervised Kohonen neural networks, and compares the result with BP neural network. Experimental results show that supervised Kohonen neural network is superior to the other two algorithms. The average recognition rate can be increased to 88.4%for five kinds of terrains.关键词
表面肌电信号/智能假肢/特征提取/有监督Kohonen神经网络/步态识别Key words
Surface electromyography (sEMG)/intelligent prosthesis/feature extraction/supervised Kohonen neural network/gait recognition引用本文复制引用
郭欣,王蕾,宣伯凯,李彩萍..基于有监督Kohonen神经网络的步态识别[J].自动化学报,2017,43(3):430-438,9.基金项目
河北省青年自然基金(F2016202327),河北省高等学校科学技术研究项目(Q2012079,ZC2016020),中国科学院人机智能协同系统重点实验室开放基金资助Supported by Natural Science Foundation of Hebei Province(F2016202327),Science Technology Research Project of Higher Education of Hebei Province(Q2012079,ZC2016020),the Open Fund of CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems （F2016202327）
