## 4 Discussion  
The current state of the art is focusing on the development of automatic techniques to discriminate pathological gait patterns from healthy ones, in order to track disease progression and prescribe target therapies [4]. In this context, machine learning algorithms \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Classification Accuracy**} \\ \hline
**Input Vector/** & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**S2**} \\ \cline{2-5}
**Classification Algorithm** & **Training a** & **Test b** & **Training a** & **Test b** \\ \hline Random Forest & 96.9\% & 90.9\% & 78.5\% & 78.8\% \\ CN2 Rule Induction & 96.9\% & 97.0\% & 63.1\% & 72.7\% \\ Neural Network & 98.5\% & 90.9\% & 83.1\% & 84.8\% \\ SVM & 95.4\% & 87.9\% & 72.3\% & 69.7\% \\ Decision Tree & 96.9\% & 93.9\% & 78.5\% & 63.6\% \\ k-NN & 94.6\% & 93.9\% & 80.0\% & 84.8\% \\ Naive Bayes & 73.8\% & 80.3\% & 61.5\% & 69.7\% \\ \hline \hline \end{tabular}  
* S1: sEMG parameters and co-contraction parameters. S2: kinematic parameters. a Classification accuracy on the training dataset. b Classification accuracy on the test dataset.  
\end{table} 
Table 2: Accuracy of the seven supervised learning algorithms for the classification of FXS-M within the FXS-FM group. Figure 4: sEMG and co-contraction parameters in which statistically significant difference (\(p<0.05\)) was found between FXS-FM and FXS-M.  
have shown the ability on one side to identify atypical gait patterns and on the other to manage multidimensional data with the possibility of increasing dataset size without the need to change the methodological approach [9; 27]. In the present contribution the standard machine learning algorithms were applied to an under-investigated pathology (FXS) from a gait-analysis perspective. In the majority of the state-of-the-art works spatiotemporal gait features have been used for gait pattern classification, accompanied by 3D gait joint angles and kinetics features [9]. Previous studies applied supervised learning algorithms driven by kinematic data acquired during gait to differentiate subjects with chronic neck pain and healthy ones [28], predict the severity of Parkinson's disease [29], categorize gait patterns in CP subjects [12; 30] and link toe-walking gait patterns to their clinical cause [31]. In the present contribution, in contrast to previous works adopting only kinematic or only sEMG features, both types of features were combined in different ways in order to fully explore their classification potential. Furthermore, to the best of our knowledge, co-contraction indexes were included for the first time in the attempt to classify gait patterns. With respect to the study aim, our results support the use of gait analysis parameters to classify FXS children and, within FXS children with full mutation, to classify children with mosaicism. In this context, the overall highest accuracy for the classification between FXS and CS subjects was found using the k-NN algorithm (100%), while for the classification between FXS-M and FXS-FM, CN2 rule induction yielded the best result (Table 2). The classification accuracy for the other algorithms (76.4-98.3% for FXS vs. CS and 69.7-93.9% for FXS-FM vs. FXS-M) was in the same range reported by previous literature in the domain of gait classification [26; 32]. The differences in terms of performance among the different algorithms were generally small (up to 6.5% for all the analysed algorithms except for naive Bayes and up to 1.7% for random forest, k-NN, SVM and decision tree in FXS vs. CS classification). The obtained results suggest that random forest, k-NN, SVM and decision tree are good candidates for future use in the development of a classification system for discriminating FXS children from CS based on their gait patterns. Meanwhile, when considering FXS-M, the CN2 rule induction was the best candidate for developing a classification system aiming to distinguish FXS-M within a group of FXS-FM individuals.