*3.5. Training the Classifier*

The present approach is based on the attempt at training the neural network classifier by means of sEMG data from 22 subjects out of 23 subjects of the present population (Learned set, LS) and then classifying gait phases in the remaining unseen subject (Unlearned set, US), following the so-called leave-one-out cross validation procedure. To this aim, all the vectors were picked up from the signals of the 22 subjects and then provided as input to the neural network for the training phase. The vectors from the remaining single subject were used for the testing phase, considering the corresponding foot-switch signal as ground truth. The procedure was performed twenty-three times, each time using a di fferent subject as test set (23 folds cross-validation). For measuring the classification performances also for learned subjects, the set was split into training set (LS-train) and test set (LS-test). In details, LS-train includes the first 90% of each subject strand (approximately 3 min and 30 s, 180 gait cycles) and LS-test the remaining 10% (approximately 30 s, 20 gait cycles). Results in each subject were provided as the classification results in a single fold. Population (global) results were provided as mean value (± SD) over the 23 folds.
