def buffer_and_predict(t, x, y, z, on_activity_detected):
global index
global sensor_data
sensor_data.append(reorient(x, y, z))
index += 1
# make sure we have exactly window_size data points :
while len(sensor_data) > window_size:
sensor_data.pop(0)
if (index >= step_size and len(sensor_data) == window_size):
activity_recognition_thread = threading.Thread(
target=predict,
args=(np.asarray(sensor_data[:]), on_activity_detected))
activity_recognition_thread.start()
index = 0
import pickle
# Load Data From Disk
print("Loading data...")
sys.stdout.flush()
data_file = os.path.join("data", "final-project-data.csv")
data = np.genfromtxt(data_file, delimiter=",")
print("Loaded {} raw labelled activity data samples.".format(len(data)))
sys.stdout.flush()
# Pre-processing
print("Reorienting accelerometer data...")
sys.stdout.flush()
reset_vars()
reoriented = np.asarray(
[reorient(data[i, 1], data[i, 2], data[i, 3]) for i in range(len(data))])
reoriented_data_with_timestamps = np.append(data[:, 0:1], reoriented, axis=1)
data = np.append(reoriented_data_with_timestamps, data[:, -1:], axis=1)
# Extract Features & Labels
# you may want to play around with the window and step sizes
window_size = 20
step_size = 20
# sampling rate for the sample data should be about 25 Hz; take a brief window to confirm this
n_samples = 1000
time_elapsed_seconds = (data[n_samples, 0] - data[0, 0]) / 1000
sampling_rate = n_samples / time_elapsed_seconds
feature_names = [
# 0, 1, 2
for json_string in json_strings:
try:
data = json.loads(json_string)
except:
previous_json = json_string
continue
previous_json = '' # reset if all were successful
sensor_type = data['sensor_type']
if (sensor_type == u"SENSOR_ACCEL"):
t = data['data']['t']
x = data['data']['x']
y = data['data']['y']
z = data['data']['z']
sensor_data.append(reorient(x, y, z))
index += 1
# make sure we have exactly window_size data points :
while len(sensor_data) > window_size:
sensor_data.pop(0)
if (index >= step_size
and len(sensor_data) == window_size):
t = threading.Thread(target=predict,
args=(np.asarray(
sensor_data[:]), ))
t.start()
index = 0
sys.stdout.flush()
except KeyboardInterrupt:
sys.stdout.flush()
data_file = 'final-data-accel.csv'
data = np.genfromtxt(data_file, delimiter=',')
print("Loaded {} raw labelled activity data samples.".format(len(data)))
sys.stdout.flush()
# %%---------------------------------------------------------------------------
#
# Pre-processing
#
# -----------------------------------------------------------------------------
print("Reorienting accelerometer data...")
sys.stdout.flush()
reset_vars()
reoriented = np.asarray([reorient(data[i,1], data[i,2], data[i,3]) for i in range(len(data))])
reoriented_data_with_timestamps = np.append(data[:,0:1],reoriented,axis=1)
data = np.append(reoriented_data_with_timestamps, data[:,-1:], axis=1)
# %%---------------------------------------------------------------------------
#
# Extract Features & Labels
#
# -----------------------------------------------------------------------------
# you may want to play around with the window and step sizes
window_size = 10
step_size = 10
# sampling rate for the sample data should be about 25 Hz; take a brief window to confirm this
if len(data) < 13:
continue
for i in range(9):
data[i] = data[i].strip()
# print(str(i) + " " + data[i])
timestamp = data[0]
accel_x = float(data[2])
accel_y = float(data[3])
accel_z = float(data[4])
gyro_x = float(data[6])
gyro_y = float(data[7])
gyro_z = float(data[8])
temp_data = reorient(accel_x, accel_y, accel_z)
temp_data.append(gyro_x)
temp_data.append(gyro_y)
temp_data.append(gyro_z)
sensor_data.append(temp_data)
index += 1
while len(sensor_data) > window_size:
sensor_data.pop(0)
if index >= step_size and len(sensor_data) == window_size:
t = threading.Thread(target=predict,
args=(np.asarray(sensor_data[:]), ))
t.start()
index = 0
