def main():
# ================== setup myo-python (do not change) =====================
myo.init(sdk_path='../../myo_sdk') # Compile Python binding to Myo's API
hub = myo.Hub() # Create a Python instance of MYO API
if not ConnectionChecker().ok: # Check connection before starting acquisition:
quit()
# =========================================================================
# Setup our custom processor of MYO's events.
# EmgBuffer will acquire new data in a buffer (queue):
listener = Buffer(buffer_len = 512) # At sampling rate of 200Hz, 512 samples correspond to ~2.5 seconds of the most recent data.
# Setup multichannel plotter for visualisation:
plotter = MultichannelPlot(nchan = 8, xlen = 512) # Number of EMG channels in MYO armband is 8
#plotter2 = MultichannelPlot(nchan = 8, xlen = 512)
N1 = 0
q = 0
j = 0
# Tell MYO API to start a parallel thread that will collect the data and
# command the MYO to start sending EMG data.
with hub.run_in_background(listener): # This is the way to associate our listener with the MYO API.
print('Streaming EMG ... Press shift-c to stop.')
while hub.running:
time.sleep(0.040)
# Pull recent EMG data from the buffer
emg_data = listener.get_emg_data()
# Transform it to numpy matrix
emg_data = np.array([x[1] for x in emg_data])
# q = q+1
# if q >= 100:
# q = 0
# N = 0
# for i in range(1,len(emg_data)):
# N[j] = N[j] + emg_data[i]
# N1[j] = N[j]/(len(emg_data))
# j = j+1
# print('\rRecording ... %d percent done.' % N[j], end='\n')
# Plot it
plotter.update_plot(emg_data.T)
# Plot Moving Mean Average Value
#plotter2.update_plot(N1,j)
plt.plot(N1,j, color="r", linestyle="-", linewidth=1)
plt.show()
if keyboard.is_pressed('C'):
print('Stop.')
break
def main():
# ================== setup myo-python (do not change) =====================
myo.init(sdk_path='../../myo_sdk') # Compile Python binding to Myo's API
hub = myo.Hub() # Create a Python instance of MYO API
if not ConnectionChecker(
).ok: # Check connection before starting acquisition:
quit()
# =========================================================================
# calculate the Mean Absolute Value
# Setup our custom processor of MYO's events.
# EmgBuffer will acquire new data in a buffer (queue):
listener = Buffer(
buffer_len=512
) # At sampling rate of 200Hz, 512 samples correspond to ~2.5 seconds of the most recent data.
# Setup multichannel plotter for visualisation:
plotter = MultichannelPlot(
nchan=8, xlen=512) # Number of EMG channels in MYO armband is 8
# Tell MYO API to start a parallel thread that will collect the data and
# command the MYO to start sending EMG data.
with hub.run_in_background(
listener
): # This is the way to associate our listener with the MYO API.
print('Streaming EMG ... Press shift-c to stop.')
while hub.running:
time.sleep(0.040)
# Pull recent EMG data from the buffer
emg_data = listener.get_emg_data()
# Transform it to numpy matrix
emg_data = np.array([x[1] for x in emg_data])
data = []
mav_data = []
for i in range(502):
data = emg_data[i:i + 10, :]
mav_data[i, :] = MAV(data)
#
#data.append(emg_data)
#data = np.array(data)
# if (data.shape[1]==512):
# mav_data = MAV(data) # 1x8
# data = np.delete(data, [0], axis=0)
# Plot it
#plotter.update_plot(emg_data.T)
plotter.update_plot(np.array(mav_data).T)
if keyboard.is_pressed('C'):
print('Stop.')
break
def main():
# ================== setup myo-python (do not change) =====================
myo.init(sdk_path='../../myo_sdk') # Compile Python binding to Myo's API
hub = myo.Hub() # Create a Python instance of MYO API
if not ConnectionChecker(
).ok: # Check connection before starting acquisition:
quit()
# =========================================================================
# Parce command line inputs, if any
input_file = 'models/trained_model.pkl'
if len(sys.argv) > 1:
input_file = sys.argv[1]
# Load pickled feature extractor and classification model
with open(input_file, 'rb') as file:
model = pickle.load(file)
# Extract variables from pickled object
mdl = model['mdl']
feature_extractor = model['feature_extractor']
gestures = model['gestures']
# Set up the buffer that will always contain the most up-to-date readings from the MYO
emg_buffer = Buffer(feature_extractor.winlen)
# Set up inference
with hub.run_in_background(emg_buffer.on_event):
print('You may start performing gestures. Press ctrl-c to stop.')
while hub.running:
time.sleep(0.050)
# Skip the rest until enough data for feature extraction is acquired
if len(emg_buffer.emg_data_queue) < feature_extractor.winlen:
continue
# Get latest emg data
emg = emg_buffer.get_emg_data()
# Convert to a numpy matrix (an Nx8 matrix, each channel is a column):
emg = np.array([x[1] for x in emg])
# Extract features from the emg signal:
feature_vector = feature_extractor.extract_feature_vector(emg)
# Use classification model to recognise the gesture:
inference = mdl.predict(feature_vector)
# Implement majority voting here, if needed:
# ...
# Output inference:
print('\rRecognized gesture: ', gestures[inference[0]], end='')
def main():
# ================== setup myo-python (do not change) =====================
myo.init(sdk_path='../../myo_sdk') # Compile Python binding to Myo's API
hub = myo.Hub() # Create a Python instance of MYO API
if not ConnectionChecker(
).ok: # Check connection before starting acquisition:
quit()
# =========================================================================
# calculate the Mean Absolute Value
# Setup our custom processor of MYO's events.
# EmgBuffer will acquire new data in a buffer (queue):
listener = Buffer(
buffer_len=512
) # At sampling rate of 200Hz, 512 samples correspond to ~2.5 seconds of the most recent data.
calculate = Feature(input_len=512)
# Setup multichannel plotter for visualisation:
plotter = MultichannelPlot(
nchan=8, xlen=512
) # Number of EMG channels in MYO armband is 8 , window size is 15 for MAV
freq = 200
move = cursor(freq)
# Tell MYO API to start a parallel thread that will collect the data and
# command the MYO to start sending EMG data.
with hub.run_in_background(
listener
): # This is the way to associate our listener with the MYO API.
print('Streaming EMG ... Press shift-c to stop.')
while hub.running:
time.sleep(0.040)
# Pull recent EMG data from the buffer
emg_data = listener.get_emg_data()
# Transform it to numpy matrix
emg_data = np.array([x[1] for x in emg_data])
# avoid len() report error
if (emg_data.ndim == 2):
if (emg_data.shape[0] == 512):
# calculate MAV of emg data
mav_data = calculate.MAV(emg_data)
mav_data = np.array(mav_data.T)
plotter.update_plot(mav_data)
move.move_cursor(mav_data)
if keyboard.is_pressed('C'):
print('Stop.')
break
from Alpes.Prosthesis import AlpesProsthesis, GRASPS
# ================== setup myo-python (do not change) =====================
myo.init(sdk_path='../../myo_sdk') # Compile Python binding to Myo's API
hub = myo.Hub() # Create a Python instance of MYO API
if not ConnectionChecker().ok: # Check connection before starting acquisition:
quit()
# =========================================================================
EMG_SAMPLING_RATE = 200
winsec = 0.1 # Window duration in seconds
winlen = int(winsec * EMG_SAMPLING_RATE) # Window length in samples
extensors_chan = 3
flexors_chan = 0
listener = Buffer(buffer_len=winlen)
mc = TwoChannelMyoControl(
# Absolute values of EMG are spanned between 0 and 127.
# thresholds are approximately measure as percentage of total muscle contraction.
# thresholds[0] corresponds to flexors, thresholds[1] correspons to extensors.
thresholds=[10, 15],
# cc_lock_duration filters out some unwanted control decisions after a co-contraction.
# cc_lock_duration is measured in number of 'winlen's (see variable above).
# Increase cc_lock_duration if unwanted motions appear after co-contraction or if
# co-contraction gets detected twice instead of once in a short amount of time.
cc_lock_duration=1)
grasps = [GRASPS().CYLINDRICAL, GRASPS().LATERAL, GRASPS().PINCH]
grasp = 0
def keyboard():
# ================== setup myo-python (do not change) =====================
myo.init(sdk_path="../../myo_sdk") # Compile Python binding to Myo's API
hub = myo.Hub() # Create a Python instance of MYO API
if not ConnectionChecker().ok: # Check connection before starting acquisition:
quit()
# =========================================================================
# Parce command line inputs, if any
input_file = "../classification/models/trained_model.pkl"
if len(sys.argv) > 1:
input_file = sys.argv[1]
# Load pickled feature extractor and classification model
with open(input_file, "rb") as file:
model = pickle.load(file)
# Extract variables from pickled object
mdl = model["mdl"]
feature_extractor = model["feature_extractor"]
gestures = model["gestures"]
# Set up the buffer that will always contain the most up-to-date readings from the MYO
emg_buffer = Buffer(feature_extractor.winlen)
# Set up inference
with hub.run_in_background(emg_buffer.on_event):
print("You may start performing gestures. Press ctrl-c to stop.")
while hub.running:
time.sleep(0.050)
# Skip the rest until enough data for feature extraction is acquired
if len(emg_buffer.emg_data_queue) < feature_extractor.winlen:
continue
# Get latest emg data
emg = emg_buffer.get_emg_data()
# Convert to a numpy matrix (an Nx8 matrix, each channel is a column):
emg = np.array([x[1] for x in emg])
# Extract features from the emg signal:
feature_vector = feature_extractor.extract_feature_vector(emg)
# Use classification model to recognise the gesture:
inference = mdl.predict(feature_vector)
# Implement majority voting here, if needed:
# ...
# Output inference:
print("\rRecognized gesture: ", gestures[inference[0]], end="")
gesture = gestures[inference[0]]
if gesture == 'rest':
continue
#pyautogui.press('w')
elif gesture == 'radial':
pyautogui.keyDown('a')
elif gesture == 'ulnar':
pyautogui.keyDown('d')
elif gesture == 'flextion':
pyautogui.keyDown('w')
elif gesture == 'extension':
pyautogui.keyDown('s')
elif gesture == 'fist':
pyautogui.keyDown('space')