## 3 Materials and Methods
### Data Pre-Processing  
Collected data were imported to MATLAB R2020a ([[https://it.mathworks.com/products/new_products/release2020a.html](https://it.mathworks.com/products/new_products/release2020a.html)]([https://it.mathworks.com/products/new_products/release2020a.html](https://it.mathworks.com/products/new_products/release2020a.html)), accessed on 18 March 2020). The inertial system provided in output its orientation in time in the form of Euler angles referred to as the reference system of the sensor itself. These data made it possible to calculate the pelvis joint kinematics, also expressed as Euler angles, referred to as the pelvis reference system. The latter is obtained by making an anticlockwise rotation of the sensor reference system's 180\({}^{\circ}\). EMG signals were processed with the following protocol: Butterworth bandpass filter  
Figure 2: Cycle of movement for lunging during the explosive lunge task (V.C.): start (**a**), _en gard_ (**b**), lunge (**c**). (**a**) Representation of the start position during explosive lunge in the test environment. (**b**) Representation of the _en gard_ position during explosive lunge in the test environment. (**c**) Representation of the final lunge position during explosive lunge in the test environment.  
with cut-off frequencies of 10 and 450 Hz and Butterworth low-pass filter with a cut-off frequency of 6 Hz. A threshold algorithm was applied to detect each lunge task's start and end. First, the Euclidean acceleration norm (Equation (1)) was calculated as follows:  
\[ACC=\sqrt{ACC_{x}^{2}+ACC_{y}^{2}+ACC_{z}^{2}} \tag{1}\]  
where \(ACC_{x}\), \(ACC_{y}\), and \(ACC_{z}\) are, respectively, the acceleration components expressed in the sensor reference system along the \(x\), \(y\), and \(z\) axes. The subject was at rest in the first 3 s of acquisition due to the necessary sensor stabilization phase. Therefore, this time window (Equation (2)) was used to calculate the mean value and the standard deviation to obtain the threshold \(T\) value as follows:  
\[T=\mu+J\times\sigma \tag{2}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of the acceleration norm during a period of inactivity (time window), and \(J\) represents a numerical constant that takes on varying values depending on the case. In this study, \(J=3\). The signal was considered a \"lunge movement\" if its over-threshold duration was greater than 70 ms. The end of the lunge coincided with the index of the last over\(-\)threshold sample, 0.2 s before the first activation was considered as the beginning of the movement for the first task; 0.3 s for the other two tasks. Therefore, both kinematic data and EMG signals were aligned in the duration of the lunge cycle. The EMG signals were normalized using the maximum peak of the EMG envelope within the movement cycle to allow multiple-subjects comparison. For each biomechanical data, a sampling of 500 samples was performed to standardize the number of samples of each signal, as this depends on how fast each subject performed the task. These biomechanical features were used to create a dataset consisting of 21 subjects and 13 features: the four EMG signals, the three components of accelerations, the three components of angular velocity, and the three components of pelvic angles, each expressed as a vector of 500 samples.  
In Figure 3, biomechanical data of an elite fencer during explosive lunge are shown. In particular, in Figure 3a accelerations on \(x\), \(y\) and \(z\)-axis are shown; Figure 3b shows angular velocities on \(x\), \(y\) and \(z\)-axes; Figure 3c shows pelvis angles on \(x\), \(y\) and \(z\)-axis and in Figure 3d target muscles envelopes are shown.