**2. Related Works**

The gold standard in gait segmentation is nowadays represented by foot pressure insoles or by footswitches [17,26–28], which allow a direct measurement of foot–floor contact. Otherwise, IMUs and EMG signals are employed as input to gait-phase identification algorithms [11,13,14,21,23]. Recently, data fusion of sensors is suggested as a further reliable approach [29,30]. Artificial intelligence techniques are also satisfactorily employed for the estimation of walking parameters [6,7,9,10,12–15]. To the best of our knowledge, no studies attempting to classify/predict gait events from only sagittal knee angles are reported in literature. For the purposes of the present work, sEMG signals are of particular interest, being measured in every gait protocol in order to characterize the neuro-muscular activity and neuro-motor disabilities and being acquired very often together with kinematic data, such as sagittal knee angles.

Not so many e fforts are available in literature, providing classification of gait phases from only sEMG signals [13,14,21,23–25]. Most of these studies aim only at classifying gait phases, not providing estimation of gait events (HS and TO). Joshi et al. introduced a control system for a foot-knee exoskeleton based on hand-crafted features computed from eight EMG signals to feed the Bayesian information criteria (BIC) [21]. Linear discriminant analysis (LDA) was then implemented to extract eight gait phases. One single subject was recruited for this experiment. The achieved accuracy ranged from 50% to 80%, with the combination of the BIC and LDA stage. Ziegier et al. employed a support-vector-machine classifier to provide binary segmentation of gait phases, based on a new bilateral feature (weighted signal di fference) from EMG signal acquired in seven muscle pairs [23]. Only two subjects were used to test the approach, walking on a treadmill at di fferent speeds. The accuracy ranged from 81% to 96% (mean value around 91%); maximum classification accuracy was identified when training and testing sets were strides from the same subject (intra-subject accuracy). Meng et al. used a hidden Markov model and set of EMG-based features to identify five gait sub-phases during treadmill walking [24]. Even in this study one single subject was used to test the classification. The best-case accuracy was 91.1%. The present group of researchers was able to achieve a mean binary-classification accuracy of 95.2%, adopting a multi-layer perceptron (MLP) classifier to interpret EMG data [25]. To this aim, an intra-subject approach was used on twelve healthy volunteers.

As far as we know, only two papers reported outcomes not only on classification of stance and swing but also on identification of heel strike and toe-o ff timing from sEMG signals [13,14]. Both studies adopted an inter-subject approach, consisting in training neural networks with sEMG signals measured during di fferent strides of a population of homogeneous subjects and then testing the classifier on brand new subjects. Nazmi at al. extracted time-frequency EMG-based features to feed a single hidden layer neural network [13]. Training set was composed of seven subjects walking on a treadmill and testing set included one single unlearned subject. Mean classification accuracy of 87.5% for learned subjects and 77% for unlearned ones were accomplished. Prediction outcomes, computed in unseen subjects, achieved a mean absolute error (MAE ± SD) of 35 ± 25 ms in assessing HS and 49 ± 15 ms in assessing TO. The present group of researchers faced the same assignment, trying to interpret the linear envelopes extracted from sEMG signals by means of a multi-layer-perceptron classifier [14]. The MLP network was trained with sEMG data acquired during walking of 22 subjects and then tested on a brand-new subject. The procedure was performed twenty-three times, each time using a di fferent subject as test-set (23-fold cross-validation). This approach provided an average (over 23-fold) binary classification accuracy of 94.9% for learned subjects and 93.4% for unlearned ones. *MAEs* in the prediction of HS and TO were 21 ± 7 ms and 38 ± 15 ms, respectively. This latter approach [14] is adopted as a reference experiment for the present study since it achieved the best performance in phase classification and gait event prediction among the inter-subject approaches reported in literature.

### **3. Materials and Methods**
