## 2 Materials and Methods
### Instrumental Assessment  
Kinematics and sEMG data were simultaneously acquired through four synchronized cameras (GoPro Hero3, 30 fps) and an sEMG system (FreeEmg, BTS, 1000 Hz) that collected the activity of tibialis anterior (TA), gastrocnemius lateralis (GL), rectus femoris (RF) and biceps femoris (BF). sEMG sensors were positioned according to [17,18] after appropriately cleaning and preparing the skin. Sensors were 24 mm in diameter and positioned 1 cm apart. Each subject performed several gait trials at a self-selected speed; at least three trials per subject were processed. The kinematic data were acquired as described in [7] without applying any markers on subjects' skin. Video sequences were processed to extract the three-dimensional anatomical landmark coordinates using the software \"Track on Field\" (BBSoF s.r.l. ), which implements the optical flow popular Kanade-Lucas-Tomasi tracking algorithm [19] in the version proposed by [PERSON] et al. (2014) [20,21]. The algorithm was modified and validated as described in [7] to enable tracking with the markers automatically added in anatomical landmarks identified by the user (Figure 1). Data were processed in Matlab(MathWorks v.2019a) and sagittal plane kinematics as well as spatiotemporal parameters were extracted according to a simplified version of [19] as in [20]. Minimal and maximal values of joint angles (hip, knee and ankle) and joints' range of motion were calculated using self-developed Matlab codes. In terms of sEMG analysis the following parameters were computed: duration of contraction, onset and offset activation timing, peak of the envelope and occurrence in the gait cycle, muscle co-contractions number and occurrence in the gait cycle [21; 22; 23; 24; 25]. The envelope was computed by low-pass filtering the signals with a fourth-order Butterworth filter and a cut-off frequency of 5 Hz. The peak of each muscle sEMG activity was determined as a percentage of medium value in the cycle (PoE%) as well as its occurrence with respect to the gait cycle (PPoE%) [21; 23]. To obtain the activation timing, signals were filtered by filter removing heartbeat, notch filter for 50 Hz, band-pass filtered with a double fifth-order Butterworth filter and full wave rectified. The cut-off frequencies varied from 5 to 15 Hz for the high-pass filter, and from 450 to 495 Hz for the low-pass filter. A double-threshold statistical detector proposed by [PERSON] [22] was applied. Background noise was estimated for each signal based on the interval of the subject's static standing. Only activation intervals longer than 30 ms were accepted [22]. The values of duration of muscle activity and the onset and offset of muscle activity intervals were detected for each gait cycle. In order to calculate the co-contraction ratio (CCR) for knee and ankle joints, Equation (1) was implemented [25]. Antagonist muscles for the knee joint were BF and GL, while RF was the agonist. For the ankle joint the agonist muscle was TA, while GL was the antagonist. An index of \(-1\) indicates a sole contribution of the agonist excitations, 1 indicates a contribution derived just from antagonist excitations and 0 indicates a balanced contribution of both agonist and antagonist excitations (i.e., the maximum value of co-contraction). Figure 1: The definition of lower limb embedded frames and sagittal plane angles. The pelvis reference system is represented in blue: the origin is the midpoint between LASIS and RASIS (MASIS), the z axis is the normalized vector oriented as the line passing through the LASIS and RASIS with its positive direction from left to right; the x axis lies in the plane defined by the RASIS, LASIS and the midpoint between the RPSIS and LPSIS (MPSIS) with its positive direction forwards; the y axis is orthogonal to the xz plane and its positive direction is proximal.