python对肌电信号进行简单的手势识别

2019-04-13 15:54发布

站内文章 / 模拟电子
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采集前臂四块不同肌肉的表面肌电信号 #加载相关模块 import pandas as pd import numpy as np import matplotlib.pyplot as plt #读取数据 data1=pd.read_excel('wq20.xlsx',sheetname=1) data2=pd.read_excel('zs20.xlsx',sheetname=1) data3=pd.read_excel('n20.xlsx',sheetname=1) data4=pd.read_excel('w20.xlsx',sheetname=1) data1.columns=['ch1','ch2','ch3','ch4'] data2.columns=['ch1','ch2','ch3','ch4'] data3.columns=['ch1','ch2','ch3','ch4'] data4.columns=['ch1','ch2','ch3','ch4'] names=locals() for i in range(1,5): plt.figure() plt.plot(names['data%s'%i]) 握拳 张手 手腕内翻 手腕外翻 将四通道的肌肉电信号汇总求其绝对值平均值，利用移动窗口法，取若干连续时间序列对应的信号强度求局域平均，若其后若干点的均值都超过一定阈值，则视为一个动作开始，反之若其后若干点的局域均值都小于阈值，则视为一个动作结束   def get_mean_semg(data): mean_semg=[] for i in range(len(data)-1): mean_semg.append((data.ch1[i]+data.ch2[i]+data.ch3[i]+data.ch4[i])/4) return mean_semg for i in range(1,5): names['mean_semg_%s'%i]=get_mean_semg(names['data%s'%i]) plt.figure() plt.plot(names['mean_semg_%s'%i]) plt.ylim(0,5) plt.savefig('a%s'%i,dpi=400) 握拳 张手 内翻 外翻   def get_move_window(mean_semg): mean_semg_arr=np.array(mean_semg) return pd.rolling_mean(mean_semg_arr,window=800) def get_break(data,i,thre,windowlenth): for i in range(i,i+windowlenth): if data[i]   获取平均值起始点，并将对应时间点作用于原始信号上，对四通道信号进行行动段提取，并将长度较小的部分过滤，视为噪音   for i in range(1,5): names['period_%s'%i]=[] names['sta_filt_%s'%i]=[] names['end_filt_%s'%i]=[] for j in range(len(names['sta_%s'%i])): names['period_%s'%i].append(names['end_%s'%i][j]-names['sta_%s'%i][j]) for k in range(len(names['period_%s'%i])): if names['period_%s'%i][k]>5000: names['sta_filt_%s'%i].append(names['sta_%s'%i][k]) names['end_filt_%s'%i].append(names['end_%s'%i][k]) for i in range(1,len(sta_filt_1)+1): names['data1_cut%s'%i]=data1[sta_filt_1[i-1]:end_filt_1[i-1]] for i in range(1,len(sta_filt_2)+1): names['data2_cut%s'%i]=data2[sta_filt_2[i-1]:end_filt_2[i-1]] for i in range(1,len(sta_filt_3)+1): names['data3_cut%s'%i]=data3[sta_filt_3[i-1]:end_filt_3[i-1]] for i in range(1,len(sta_filt_4)+1): names['data4_cut%s'%i]=data4[sta_filt_4[i-1]:end_filt_4[i-1]] plt.figure(figsize=(50,3)) for i in range(1,21): plt.subplot2grid((1,20),(0,i-1),colspan=1).plot(names['data1_cut%s'%i]) plt.ylim(0,10) plt.title('fist') plt.figure(figsize=(50,3)) for i in range(1,22): plt.subplot2grid((1,21),(0,i-1),colspan=1).plot(names['data2_cut%s'%i]) plt.ylim(0,10) plt.title('open') plt.figure(figsize=(50,3)) for i in range(1,25): plt.subplot2grid((1,24),(0,i-1),colspan=1).plot(names['data3_cut%s'%i]) plt.ylim(0,10) plt.title('toright') plt.figure(figsize=(50,3)) for i in range(1,21): plt.subplot2grid((1,20),(0,i-1),colspan=1).plot(names['data4_cut%s'%i]) plt.ylim(0,10) plt.title('toleft') 握拳 张手 内弯 外翻 对各通道行动段求区间的平均值MAV，可以看出对于不同的动作，MAV值区别明显，可以作为特征向量对信号进行特征提取   mav_fist=pd.DataFrame(columns=['ch1','ch2','ch3','ch4'],index=[np.arange(20)]) for i in range(1,21): mav_fist.loc[i-1,'ch1']=names['data1_cut%s'%i].ch1.mean() mav_fist.loc[i-1,'ch2']=names['data1_cut%s'%i].ch2.mean() mav_fist.loc[i-1,'ch3']=names['data1_cut%s'%i].ch3.mean() mav_fist.loc[i-1,'ch4']=names['data1_cut%s'%i].ch4.mean() mav_open=pd.DataFrame(columns=['ch1','ch2','ch3','ch4'],index=[np.arange(21)]) for i in range(1,22): mav_open.loc[i-1,'ch1']=names['data2_cut%s'%i].ch1.mean() mav_open.loc[i-1,'ch2']=names['data2_cut%s'%i].ch2.mean() mav_open.loc[i-1,'ch3']=names['data2_cut%s'%i].ch3.mean() mav_open.loc[i-1,'ch4']=names['data2_cut%s'%i].ch4.mean() mav_toright=pd.DataFrame(columns=['ch1','ch2','ch3','ch4'],index=[np.arange(24)]) for i in range(1,25): mav_toright.loc[i-1,'ch1']=names['data3_cut%s'%i].ch1.mean() mav_toright.loc[i-1,'ch2']=names['data3_cut%s'%i].ch2.mean() mav_toright.loc[i-1,'ch3']=names['data3_cut%s'%i].ch3.mean() mav_toright.loc[i-1,'ch4']=names['data3_cut%s'%i].ch4.mean() mav_toleft=pd.DataFrame(columns=['ch1','ch2','ch3','ch4'],index=[np.arange(20)]) for i in range(1,21): mav_toleft.loc[i-1,'ch1']=names['data4_cut%s'%i].ch1.mean() mav_toleft.loc[i-1,'ch2']=names['data4_cut%s'%i].ch2.mean() mav_toleft.loc[i-1,'ch3']=names['data4_cut%s'%i].ch3.mean() mav_toleft.loc[i-1,'ch4']=names['data4_cut%s'%i].ch4.mean()     plt.figure(figsize=(20,5)) mav_fist_ax=plt.subplot2grid((1,4),(0,0),colspan=1) mav_fist_ax.scatter(x=np.arange(20),y=mav_fist.ch1,c='r') mav_fist_ax.scatter(x=np.arange(20),y=mav_fist.ch2,c='g') mav_fist_ax.scatter(x=np.arange(20),y=mav_fist.ch3,c='b') mav_fist_ax.scatter(x=np.arange(20),y=mav_fist.ch4,c='y') mav_open_ax=plt.subplot2grid((1,4),(0,1),colspan=1) mav_open_ax.scatter(x=np.arange(21),y=mav_open.ch1,c='r') mav_open_ax.scatter(x=np.arange(21),y=mav_open.ch2,c='g') mav_open_ax.scatter(x=np.arange(21),y=mav_open.ch3,c='b') mav_open_ax.scatter(x=np.arange(21),y=mav_open.ch4,c='y') mav_toright_ax=plt.subplot2grid((1,4),(0,2),colspan=1) mav_toright_ax.scatter(x=np.arange(24),y=mav_toright.ch1,c='r') mav_toright_ax.scatter(x=np.arange(24),y=mav_toright.ch2,c='g') mav_toright_ax.scatter(x=np.arange(24),y=mav_toright.ch3,c='b') mav_toright_ax.scatter(x=np.arange(24),y=mav_toright.ch4,c='y') mav_toleft_ax=plt.subplot2grid((1,4),(0,3),colspan=1) mav_toleft_ax.scatter(x=np.arange(20),y=mav_toleft.ch1,c='r') mav_toleft_ax.scatter(x=np.arange(20),y=mav_toleft.ch2,c='g') mav_toleft_ax.scatter(x=np.arange(20),y=mav_toleft.ch3,c='b') mav_toleft_ax.scatter(x=np.arange(20),y=mav_toleft.ch4,c='y')   mav_fist['action']=0 mav_open['action']=1 mav_toright['action']=2 mav_toleft['action']=3 sumup=mav_fist.append([mav_open,mav_toright,mav_toleft],ignore_index=True) y=sumup.action x=sumup.drop(['action'],axis=1) from sklearn.model_selection import train_test_split import xgboost as xgb train_x,test_x,train_y,test_y=train_test_split(x.as_matrix(),y.as_matrix(),test_size=0.2) xg_train=xgb.DMatrix(train_x,label=train_y) xg_test=xgb.DMatrix(test_x,label=test_y) param = {} param['objective'] ='multi:softmax' param['eta']=0.1 param['max_depth']=6 param['silent']=1 param['nthread']=4 param['num_class']=4 watchlist = [(xg_train, 'train'), (xg_test, 'test')] num_round=5 bst = xgb.train(param, xg_train, num_round, watchlist) pred = bst.predict(xg_test)
对四个不同的手势进行数字命名，通过xgboost进行训练分析，16个测试样的预测结果正确率为100% 更多Python视频、源码、资料加群683380553免费获取 转载至：https://zhuanlan.zhihu.com/p/41073513

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