3.4.1. Knee Approach

For suitably training the classifier, each knee-angle signal was split into 20-sample windows (corresponding to 10 ms). A chronological sequence of 40-sample vectors was created, where each vector included the two synchronized 20-sample windows from two knee-angle signals (right and left leg). In details, the first sample of the first 40-sample vector of the sequence was the first sample of the knee angle measured in the right leg; the second sample of the first 40-sample vector was the first sample of the knee angle measured in the left leg.

Then, a specific label was assigned to each 40-sample window as follows: if the value of all the samples of the basographic signal corresponding to the 40-sample vector was 0 (or 1), a global label 0 (or 1) was assigned to the 40-sample vector. 40-sample vectors, including swing-to-stance or stance-to-swing transitions, were discarded. This approach including only knee-angle data to feed the neural network is referred to as 'Knee approach'.
