Fig. 3

A 20-s segment of pressure sensor and right TA EMG data from Subject 1. The red line in the figure corresponds to the time of gait starting, while the black line corresponds to the time of gait stopping. The resting and walking times were chosen by taking equidistant points from two adjacent gait starting and stopping times. After marking all crucial time points, data windows of different lengths, and relative positions corresponding to those events were taken for further processing and feature extraction. In the figure, the segmentation procedure of [-1,0] data interval is shown. The data windows were marked by -1 and 0 where 0 denotes the extracted event times and -1 denotes the time points one second before the event. The extracted data windows were labeled to the corresponding events. After that, two two-class classification problems were addressed: ’Rest vs. Start’ and ’Walk vs. Stop.’ This segmentation and windowing approach ensured no overlapping between data windows corresponding to different classes reducing the chance of data contamination