3.4.2. KEMG Approach

A similar approach was used when both knee angles and sEMG signals were used to train the network. Each signal (knee angle and sEMG) was split into 20-sample windows. A chronological sequence of 200-sample vectors was created, where each vector included the ten synchronized 20-sample windows from the sEMG signals of the eight muscles (four for each leg) and two knee-angle signals. In details, the first sample of the first 200-sample vector of the sequence was the first sample of the knee angle measured in the right leg; the second sample of the first 200-sample vector was the first sample of the EMG signal from the muscle 1 (TA, right leg), and so on up to the 10th signal (MH, left leg). Then a specific label was assigned to each 200-sample vector as follows: if the value of all the samples of the basographic signal corresponding to the 200-sample vector was 0 (or 1), a global label 0 (or 1) was assigned to the 200-sample vector. 200-sample vectors including swing-to-stance or stance-to-swing transitions were discarded. This approach including both Knee-angle and sEMG data to feed the neural network is referred to as KEMG approach.
