摘要
针对仿生假肢动作识别问题,提出基于时频广义S变换和VL-MOBP神经网络的下肢动作识别方法。首先用时频广义S变换对年龄在20~40岁,身高在170~185 cm,体重在50~75 kg的22名男性测试者下肢4种表面肌电信号和膝盖弯曲度信号进行多分辨率分析,得到在时间和频率分辨率较好情况下信号时频累计特性曲线,然后提取时频累计特性曲线幅值的均值和标准差作为特征向量,用VL-MOBP神经网络对人体下肢的行走、站立及静坐3种动作进行识别。实验结果表明,提出的下肢动作识别方法能够取得很好的识别效果,平均识别准确度达96.67%,高出小波变换约56%,高出短时傅里叶变换约36%,验证了该方法在动作识别中的有效性。
Aiming at the needs of bionic prosthetic motion recognition,a lower limb motion recognition method based on time-frequency generalized S transform and VL-MOBP neural network was proposed.First,time-frequency generalized S-transform was used to measure 4 kinds of surface electromyographic signals and knee flexion of the lower extremities of 22 male subjects aged between 20 and 40 years old,between 170 cm and 185 cm tall and weight between 50 kg and 75 kg.Using multi-resolution analysis of the frequency signal to obtain the time-frequency cumulative characteristic curve of the signal when the time and frequency resolution were good,then extracting the mean and standard deviation of the amplitude of the time-frequency cumulative characteristic curve as the feature vector,and using the VL-MOBP neural network to recognize the three movements of human lower limbs:Walking,standing,and sitting.The experimental results showed that the proposed lower limb movement recognition method can achieve good recognition results,with an average recognition accuracy of 96.67%,which is about 56%higher than the wavelet transform and about 36%higher than the short-time Fourier transform.Effectiveness in motion recognition has been verified.
作者
尹柏强
邓影
王署东
胡增超
李兵
佐磊
Yin Baiqiang;Deng Ying;Wang Shudong;Hu Zengchao;Li Bing;Zuo Lei(School of Electrical and Automation Engineering,Hefei University of Technology,Hefei 230009,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第11期1-9,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61971175)
国家自然科学基金重点项目(51637004)
国家重点研发计划“重大科学仪器设备开发”项目(2016YFF0102200)
中央高校基本科研业务费(JZ2019YYPY0025)资助项目
关键词
时频广义S变换
VL-MOBP神经网络
表面肌电信号
动作识别
time-frequency generalized S transform
VL-MOBP neural network
surface electromyographic
action recognition
