nChannels = 2
nSignals = nGestures*nIterations*nChannels
emg = []
segmented_emg = []
for m in range(1,nGestures+1):
for i in range(nIterations):
for c in range(1,nChannels+1):
emg.append(emg_data['motion'+str(m)+'_ch'+str(c)][:,i]) #motion1_ch1_i1, motion1_ch2_i1, motion1_ch1_i2, motion1_ch2_i2
#for z in range(nSignals):
# emg[z] = emg[z]*(5/2)/2**24
# Segmentation
for n in range(nSignals):
segmented_emg.append(fex.segmentation(emg[n]))
# Feature calculation
feature_list = [fex.mav, fex.rms, fex.var, fex.ssi, fex.zc, fex.wl, fex.ssc, fex.wamp]
nSegments = len(segmented_emg[0][0])
nFeatures = len(feature_list)
feature_matrix = np.zeros((nGestures*nIterations*nSegments,nFeatures*nChannels))
n = 0
for i in range(0,nSignals,nChannels):
for j in range(nSegments):
feature_matrix[n] = fex.features((segmented_emg[i][:,j],segmented_emg[i+1][:,j]),feature_list)
n = n + 1
# Target matrix generation
emg_data = sio.loadmat('emg_data_5_class_2ch')
#emg_data = sio.loadmat('emg_25-11-15')
nMotions = 5
nChannels = 2
nIterations = 4
emg = []
emg_seg = []
for m in range(1,nMotions+1):
for i in range(nIterations):
for c in range(1,nChannels+1):
emg.append(emg_data['motion'+str(m)+'_ch'+str(c)][:,i]) #motion1_ch1_i1, motion1_ch2_i1, motion1_ch1_i2, motion1_ch2_i2
for n in range(len(emg)):
emg_seg.append(fex.segmentation(emg[n]))
feature_list = [fex.mav, fex.rms, fex.var, fex.ssi, fex.zc, fex.wl, fex.ssc, fex.wamp]
nSegments = len(emg_seg[0][0])
nFeatures = len(feature_list)
feature_matrix = np.zeros((nSegments*nIterations*nMotions,nFeatures*nChannels))
n = 0
for i in range(0,len(emg_seg),nChannels):
for j in range(nSegments):
feature_matrix[n] = fex.features((emg_seg[i][:,j],emg_seg[i+1][:,j]),feature_list)
n += 1
emg = []
segmented_emg = []
for m in range(1, nGestures + 1):
for i in range(nIterations):
for c in range(1, nChannels + 1):
emg.append(
emg_data['motion' + str(m) + '_ch' + str(c)][:, i]
) #motion1_ch1_i1, motion1_ch2_i1, motion1_ch1_i2, motion1_ch2_i2
#for z in range(nSignals):
# emg[z] = emg[z]*(5/2)/2**24
# Segmentation
for n in range(nSignals):
segmented_emg.append(fex.segmentation(emg[n]))
# Feature calculation
feature_list = [
fex.mav, fex.rms, fex.var, fex.ssi, fex.zc, fex.wl, fex.ssc, fex.wamp
]
nSegments = len(segmented_emg[0][0])
nFeatures = len(feature_list)
feature_matrix = np.zeros(
(nGestures * nIterations * nSegments, nFeatures * nChannels))
n = 0
for i in range(0, nSignals, nChannels):
for j in range(nSegments):
feature_matrix[n] = fex.features(
# for i in range(n_iterations):
# for c in range(1,n_channels+1):
# emg.append(emg_data['motion'+str(m)+'_ch'+str(c)][:,i]) #motion1_ch1_i1, motion1_ch2_i1, motion1_ch1_i2, motion1_ch2_i2
for g in emg_data.keys():
class_labels.append(g)
for i in range(n_iterations):
for c in range(n_channels):
emg.append(np.array(zip(*emg_data[g][i])[c][0:999]))
#for z in range(n_signals):
# emg[z] = emg[z]*(5/2)/2**24
# Segmentation
for n in range(n_signals):
segmented_emg.append(fex.segmentation(emg[n],n_samples=50))
# Feature calculation
feature_list = [fex.mav, fex.rms, fex.var, fex.ssi, fex.zc, fex.wl, fex.ssc, fex.wamp]
n_segments = len(segmented_emg[0][0])
for i in range(0,n_signals,n_channels):
if len(segmented_emg[i][0]) < n_segments :
n_segments = len(segmented_emg[i][0])
n_features = len(feature_list)
feature_matrix = np.zeros((n_classes*n_iterations*n_segments,n_features*n_channels))
n = 0
for i in range(0,n_signals,n_channels):
for j in range(n_segments):
feature_matrix[n] = fex.features((segmented_emg[i][:,j],
def process_emg(data):
global count, emg, classifier, class_labels
if count < 4:
count += 1
emg.append(np.array(data[0]))
emg.append(np.array(data[1]))
else:
count = 0
n_classes = 1
n_iterations = 1
n_channels = 8
#n_signals = 8
n_signals = n_classes * n_iterations * n_channels
segmented_emg = list()
print("EMG: ")
print(emg)
# Segmentation
for n in range(n_signals):
segmented_emg.append(fex.segmentation(emg[n], n_samples=1))
# Feature calculation
feature_list = [
fex.mav, fex.rms, fex.var, fex.ssi, fex.zc, fex.wl, fex.ssc,
fex.wamp
]
n_segments = len(segmented_emg[0][0])
print("\nN_Segments: " + str(n_segments) + "\n")
n_features = len(feature_list)
feature_matrix = np.zeros(
(n_classes * n_iterations * n_segments, n_features * n_channels))
n = 0
print("Feature_Matrix_SHAPE: ")
print(feature_matrix.shape)
print("")
print("Segmented_EMG: ")
print(segmented_emg)
print("")
for i in range(0, n_classes):
for j in range(n_segments):
#print("i: " + str(i))
#print("j: " + str(j))
feature_matrix[n] = fex.features(
(segmented_emg[i][:, j], segmented_emg[i + 1][:, j],
segmented_emg[i + 2][:, j], segmented_emg[i + 3][:, j],
segmented_emg[i + 4][:, j], segmented_emg[i + 5][:, j],
segmented_emg[i + 6][:, j], segmented_emg[i + 7][:, j]),
feature_list)
n = n + 1
# Target matrix generation
print(feature_matrix)
print(feature_matrix.shape)
y = fex.generate_target(n_iterations * n_segments, class_labels)
print(y)
print(y.shape)
# Dimensionality reduction and feature scaling
[X, reductor, scaler] = fex.feature_scaling(feature_matrix, y)
# Classification
print(X)
print(X.shape)
predict = classifier.predict(feature_matrix)
print(predict)
emg = list()