Table 2.

Brief description of the gait partitioning methods adopted in this comparative study.

	Typology	Sensor	Signals	Filters	Method Description
S-method [26]	Threshold	Single gyroscope	Sagittal angular velocity of foot	2nd order low-pass Butterworth filter with 15 Hz cut off frequency	Absolute value of the reference signal is used for identifying gait events. Starting from FS, the HO/TS time instants occurred when the absolute value of angular velocity exceed/was less than 30°/s, respectively. TO was the maximum value of the angular velocity in the clockwise direction after HO. After mid-swing, HS was identified as the second maximum value of the angular velocity in the clockwise direction.
R-method [22]	Threshold	Single 3-axes accelerometer	Radial and tangential component of foot acceleration	2nd order low-pass Butterworth filter with 6 Hz cut off frequency and two low pass moving average filters.	Gait events are identified by processing five reference signals: (i) the resultant acceleration filtered with the 2nd order low-pass Butterworth with 6 Hz cut off frequency (c50); (ii) its 1st and (iii) 2nd derivatives; and, the resultant acceleration filtered with low pass moving average filter with 1.25 s (iv) and 0.30 s (v) of window length (cA200 and cA50, respectively). The 1st and 2nd derivatives of resultant acceleration provide turning and inflection points needed to gait events identification. The two moving average filtered signals provide constraining ranges allowing the correct identification of turning and inflection points identification.
HMMsst [28]	Machine learning	Single gyroscope	Sagittal angular velocity of foot	2nd order low-pass Butterworth filter with 17 Hz cut off frequency	This method processes the sagittal angular velocity of the foot, based on the scalar continuous Hidden Model Markov (cHMM). Gait phases are obtained as the likely sequence of the hidden states. The starting point is the training procedure involving the Baum-Welch algorithm and a set of model parameters: (i) the probability distribution matrix of the transition state, chosen as a left-right model; (ii) the initial state vector distribution, chosen giving the same probability for all phases, (iii) a vector of mixture coefficients, i.e., the weights used to estimate the sequence of states; and (iv) the mean and the standard deviation of the signal. This algorithm is trained with signals gathered from two trials of a patient in a specific pharmacological condition (OFF or ON), and tested with leaved out trial of the same patient.
HMMspt [24]	Machine learning	Single gyroscope	Sagittal angular velocity of foot	2nd order low-pass Butterworth filter with 17 Hz cut off frequency	Similar to the previous method, a scalar continuous Hidden Model Markov is used to estimate gait sequence as the likely sequence. For patients with motor deficit, the training procedure involves mean and standard deviation of foot sagittal angular velocity of all trials of control group. Afterwards, foot sagittal angular velocity of all trials of patients is tested to estimate likely gait sequence.