wear.py

import matplotlib
matplotlib.use('TkAgg')
import mne
import h5py
import numpy as np
import matplotlib.pyplot as plt
import numpy.typing as npt
plt.ion()

def zero_detector(arr: npt.NDArray[np.uint]) -> list([npt.NDArray[np.uint],
                                                      npt.NDArray[np.uint]]):
    """Return list of time windows where values are zeros.

    Args:
        arr: Given 1D array
    Returns:
        windows: median absolute deviation
    """
    # Create an array that is 1 where a is 0, and pad each end with an extra 0.
    iszero = np.concatenate(([0], np.equal(arr, 0).view(np.int8), [0]))
    absdiff = np.abs(np.diff(iszero))
    # Runs start and end where absdiff is 1.
    windows = np.where(absdiff == 1)[0].reshape(-1, 2)
    return windows


h5_file = 'JoEE_wear_bed_2021-11-01\JoEE_wear_bed_2021-11-01.h5'
data = dict()

hf = h5py.File(h5_file, "r" ) 
data['accel'] = np.array(hf.get( "accelerometerAdxlRaw"))
data['fs'] = np.int(np.array(hf.get("sampleRate" ))[0][0])
data['temp'] = np.array(hf.get("temperatureAdxlRaw" ))
data['wear_nowear'] = np.array(hf.get("wearTruth" ))
data['in_bed'] = np.array(hf.get("inBedTruth"))
hf.close()
data['accel_filt'] = mne.filter.filter_data(data['accel'][1:], data['fs'], 0.05, 1).T
time_mins = (data['accel'][0] - data['accel'][0][0])/60


time = np.arange(0, len(data['accel'].T)/data['fs'] , 1/data['fs'])
file = h5py.File('test_wear1.h5', 'w')
file.create_dataset('acceleration', data=data['accel'][1:, :3200])
file.create_dataset('temperature', data=data['temp'][1:, :3200])
file.create_dataset('wear_truth', data=data['wear_nowear'][1:, :3200])
file.create_dataset('time', data=data['accel'][0, :3200])
file.create_dataset('sfreq', data=data['fs'])
file.close()

 


win = int(data['fs'] * 60) # One Minute
step = data['fs'] # One seconds
len_data =  data['temp'].shape[1]

win_accel = []
win_temp = []
win_time = []

for index in range(0, len_data - win, step):
    win_accel.append(np.mean(np.abs(np.std(data['accel_filt'][index:index+win, :], axis = 0))))
    win_temp.append(np.mean(data['temp'][1, index:index+win]))
    win_time.append(np.mean(time_mins[index:index+win]))
win_accel = np.array(win_accel)
win_temp = np.array(win_temp)
win_temp = np.array(win_temp)

predict = np.int8(np.bitwise_and(win_temp > 25, win_accel*1000 > 2.1))
non_wear_wins = zero_detector(predict)
for non_wear_win in non_wear_wins:
    if (non_wear_win[1] - non_wear_win[0]) < 800:
        predict[non_wear_win[0]:non_wear_win[1]] = predict[non_wear_win[0]-1]

wear_wins = zero_detector(1 - predict)
for wear_win in wear_wins:
    if (wear_win[1] - wear_win[0]) < 2400:
        predict[wear_win[0]:wear_win[1]] = predict[wear_win[0]-1]

plt.figure()
params = {'legend.fontsize': 'x-large',
          'figure.figsize': (16, 8),
         'axes.labelsize': 'xx-large',
         'axes.titlesize':'xx-large',
         'xtick.labelsize':'xx-large',
         'ytick.labelsize':'xx-large'}
plt.rcParams.update(params)

plt.plot(time_mins, data['accel_filt'] + 25, 'r', label='Acceleration (g) + 25')
plt.plot(time_mins, data['temp'][1],'g', label='Temperature (C)')
plt.plot(time_mins, data['wear_nowear'][1]*2+25, 'k', linewidth=4, label='Truth (25 Non-wear, 27 Wear)')
plt.plot(win_time, predict*2+25, 'm', label='Prediction (25 Non-wear, 27 Wear)',linewidth=4)
plt.xlabel('Time (min)')
plt.ylabel('Temperature (C)')
plt.xlim([time_mins[0], time_mins[-1]])
plt.ylim([15, 35])
plt.legend()
plt.tight_layout()