def Recog_Data(filename):
# filename: string
raw_data = readfile.loadfile(filename)
data = Calfoot.Cal_foot(raw_data)
p1, p2 = [], []
for i in range(len(data['Position_r'])):
p1.append(np.sqrt(np.sum(np.square(data['Position_r'][i, :]))))
p2.append(np.sqrt(np.sum(np.square(data['Position_l'][i, :]))))
i = 0
while p1[i] == 0 and p2[i] == 0:
i = i + 1
j = len(p1) - 1
last_p1, last_p2 = p1[-1], p2[-1]
while abs(p1[j] - last_p1) < 0.01 and abs(p2[j] - last_p2) < 0.01:
j = j - 1
Angle = Calangle.Cal_angle(1, raw_data)[i:j, :]
# Length of the sequence
seq_len = 25
data_temp = []
for index in range(len(Angle) - seq_len + 1):
data_temp.append(Angle[index:index + seq_len, :])
data_temp = np.array(data_temp)
data = {}
data['x_action'] = data_temp
return data
def Detect_phase(filename):
# filename: string
raw_data = readfile.loadfile(filename)
Angle = Calangle.Cal_angle(1, raw_data)
Foot = Calfoot.Cal_foot(raw_data)
# Determine when the person is walking
p1, p2 = [], []
for i in range(len(Foot['Position_r'])):
p1.append(np.sqrt(np.sum(np.square(Foot['Position_r'][i, :]))))
p2.append(np.sqrt(np.sum(np.square(Foot['Position_l'][i, :]))))
i = 0
while p1[i] == 0 and p2[i] == 0:
i = i + 1
j = len(p1) - 1
last_p1, last_p2 = p1[-1], p2[-1]
while abs(p1[j] - last_p1) < 0.01 and abs(p2[j] - last_p2) < 0.01:
j = j - 1
data = np.zeros([i]).tolist()
temp = np.zeros([len(Foot['Position_r']) - j]).tolist()
detect_data = phase_detect(Angle[i:j, :])
data = data + detect_data + temp
return data
def Process_data(filename):
# filename string
raw_data = readfile.loadfile(filename)
data = Calfoot.Cal_foot(raw_data)
p1, p2 = [], []
for i in range(len(data['Position_r'])):
p1.append(np.sqrt(np.sum(np.square(data['Position_r'][i, :]))))
p2.append(np.sqrt(np.sum(np.square(data['Position_l'][i, :]))))
i = 0
while p1[i] == 0 and p2[i] == 0:
i = i + 1
j = len(p1) - 1
last_p1, last_p2 = p1[-1], p2[-1]
while abs(p1[j] - last_p1) < 0.01 and abs(p2[j] - last_p2) < 0.01:
j = j - 1
Angle = Calangle.Cal_angle(1, raw_data)[i:j, :]
temp = np.zeros([Angle.shape[0], 1])
if filename[0:3] == 'zou':
temp = np.append(temp + 1, np.zeros([Angle.shape[0], 3]), 1)
elif filename[0:3] == 'lou':
temp = np.append(temp, temp + 1, 1)
temp = np.append(temp, np.zeros([Angle.shape[0], 2]), 1)
elif filename[0:3] == 'zuo':
temp = np.append(np.zeros([Angle.shape[0], 2]), temp + 1, 1)
temp = np.append(temp, np.zeros([Angle.shape[0], 1]), 1)
elif filename[0:3] == 'you':
temp = np.append(np.zeros([Angle.shape[0], 3]), temp + 1, 1)
Angle = np.append(Angle, temp, 1)
# Length of the sequence
seq_len = 25
data_temp = []
for index in range(len(Angle) - seq_len + 1):
data_temp.append(Angle[index:index + seq_len, :])
data = np.array(data_temp)
return data
import Calangle # Calculate the angle of each joint import Calfoot # Calculate the state and the position of each foot import CorrectData # Pre-process the data for correcting import Correct # Correct the Knee angle import TrainCorrect # Train the model import readfile # Read the raw file and return a list(N * 9) import h5py filename = 'zou4.TXT' raw_data = readfile.loadfile(filename) # raw_data is a list(N * 9) Angle = Calangle.Cal_angle(1, raw_data) # Angle is a list((N/7) * 14) Foot = Calfoot.Cal_foot(raw_data) # Foot is a dictionary, the keys are 'Stationary_r', 'Position_l', 'Position_r', 'Stationary_l' # which mean whether the foot is on the field and what the position of each foot is modelname = '' Data_for_Cor = CorrectData.Correct_Data(raw_data) # Data for Correcting the Knee joint Data_for_Tra = CorrectData.Train_Correct_Data(raw_data) # Data for Training the model TrainCorrect.Train_model(Data_for_Tra, modelname) # Train or update the model and save it # The previous one is saved 'modelname_old' Corrected_Data = Correct.Correct(Data_for_Cor, modelname) # Return the corrected angle data, which has the same size with Angle
thigh_l_len = thigh_r_len = Upper_leg_length
crus_l_len = crus_r_len = Lower_leg_length
foot_l_len = foot_r_len = foot_size
hipjoint_len = width
bodydata = {
str(thigh_l_len): thigh_l_len,
str(thigh_r_len): thigh_r_len,
str(crus_l_len): crus_l_len,
str(crus_r_len): crus_r_len,
str(foot_l_len): foot_l_len,
str(foot_r_len): foot_r_len,
str(hipjoint_len): hipjoint_len
}
data = readfile.loadfile('zou41.txt')
data = np.array(data)
acc = np.delete(data, [4, 5, 6, 7], 1)
acc = acc.tolist()
qu = np.delete(data, [1, 2, 3], 1)
qu = qu.tolist()
postdata = {
'name': 'test',
'gather_time': '2018-5-22T21:00',
'device_id': '1',
'exercise_id': '7',
'acceleration': str(acc),
'quaternians': str(qu),
'myoelectricity': ''
}
re = PostData.postfile(url, header, 'firsthand', postdata)