TABLE 2.

Features extracted from the inertial readings.

Features	Python code
Time domain
Range	range = values.max() - values.min()
Standard deviation	std = values.std()
Root mean square	rms = numpy.sqrt(numpy.mean(values∗∗2))
Skewness	sk = scipy.stats.skew(values)
Kurtosis	kt = scipy.stats.kurtosis(values)
Linear prediction coefficients	lp_coefs = librosa.lpc(values, 3)
Wavelet transform detail coefficients (cD)	_, cD = pywt.dwt(values, ’db3’)
cD variance	variance = numpy.var(cD)
cD entropy	def approximate_entropy(U, m = 2, r = 3):
	U = numpy.array(U)
	N = U.shape[0]
	def phi(m):
	z = N - m + 1.0
	x = numpy.array([U[i:i + m] \
	for i in range(int(z))])
	x_ = numpy.repeat(x[:, \
	numpy.newaxis], 1, axis = 2)
	C = numpy.sum(numpy.absolute(x - \
	x_).max(axis = 2) < = r, \
	axis = 0)/z
	return numpy.log(C).sum()/z
	entropy = abs(phi(m + 1) - phi(m))
Third order cumulant	third_order_cum = scipy.stats.moment(values, moment = 3)
Temporal frequency (tf) domain
Peak of energy	p_tf = frequency_values.max()
Frequency at the peak energy	xf = numpy.linspace(0, af/2,
	frequency_values.size)
	tf_p = xf[numpy.argmax(frequency_values)]
Skewness_tf	sk_tf = scipy.stats.skew(frequency_values)
Kurtosis_tf	kt_tf = scipy.stats.kurtosis(frequency_values)
Mean frequency	def mean_frequency(frequency_values):
	xf = numpy.linspace(0, af/2,
	frequency_values.size)
	xf = xf[xf > = 1]
	total_area = numpy.trapz(frequency_values, xf)
	for i, x in enumerate(xf):
	partial_area = numpy.trapz(frequency_values[:i],
	xf[:i])
	if partial_area > total_area/2:
	mean_freq = xf[i-1]
Power ratio (1–6 Hz/6–12 Hz)	xf = numpy.linspace(0, af/2,
	frequency_values.size)
	num = frequency_values[(xf > = 1) &
	(xf < = 6)]
	den = frequency_values[(xf > = 6) &
	(xf < = 12)]
	power_ratio = num.mean()/den.mean()

values, inertial measures in the time domain vector; frequency_values, inertial measures in the temporal frequency domain vector; af, the acquisition frequency; and, xf, frequency values vector.