Table 1.

Three feature sets were used as inputs to SVM. 1) General features, 2) Selected features.

	General features	Domain knowledge based selected features
Data input for feature extraction	Accelerometer (Ax, Ay, Az) and gyroscope (Gx, Gy, Gz) signals in all 3 directions of normalized RGC	- Resultant acceleration $(R=\sqrt{A_x^2+A_y^2+A_z^2}$ - Resultant Jerk $(J=\frac{dR}{dt})$
	Mean Standard deviation Maximum Minimum Mean absolute value $\overline{x}=\frac{1}{N}\sum _{k=1}^N|x_k|$ Skewness Kurtosis Energy Number of slope sign changes Number of zero crossings Length of waveform Dominant frequency using low-pass filter and FFT	Resultant acceleration features Skewness (temporal shift) Energy Dominant frequency Maximum acceleration Minimum acceleration Range of acceleration
		Resultant jerk features Skewness (temporal shift) Mean jerk at heel contact Absolute maximum jerk Absolute minimum jerk Range of jerk produced abs (max-min) Jerk cost $JC={\int }_0^T|\frac{d^{3}r}{d{t}^3}{|}^{2}dt$