## 2 Materials and Methods
### Data Analysis  
Vertical GRF data were captured using a tri-axial force platform (sampling rate: 1000 Hz) connected to a signal conditioner (Gen 5, Advanced Mechanical Technology Inc., Watertown, MA, USA) and recorded with data acquisition software (NetForce, Advanced Mechanical Technology Inc., Watertown, MA, USA) [26]. The platform was configured to collect data for 10 s per trial, with sampling initiated manually to ensure full capture of each CMJ trial. Each IMU recorded tri-axial angular velocity using 16-bit micro-electromechanical systems (MEMS) gyroscopes (\(\pm\)2000\({}^{\circ}\)s\({}^{-1}\) range, 1600 Hz sampling frequency) and tri-axial linear acceleration using 16-bit low-g accelerometers (\(\pm\)16 g range, 1600 Hz sampling frequency), storing the data in internal memory. Time synchronization and IMU sampling were manually controlled via Capture.U software (Version 1.4.1, Vicon Motion Systems Ltd., Oxford, UK) installed on an iPad Pro (Apple Inc., Cupertino, CA, USA) [26]. Signal processing of vertical GRF data was performed using a custom MATLAB(r) script (Version R2023a, The MathWorks Inc., Natick, MA, USA). GRF data were first converted to vertical acceleration by dividing the GRF values by the participant's body mass [26]. The vertical acceleration data were then filtered using a 4 th-order recursive low-pass Butterworth filter with a cut-off frequency of 50 Hz, determined through residual analysis optimization [30]. The filtered vertical acceleration data were trimmed to span from the initiation of the countermovement to the onset of the flight phase of the CMJ. CMJ initiation was identified by estimating a 5-standard-deviation range around 0.5 s of static data. The initiation point was defined as the first time the GRF magnitude crossed below the lower bound of this range [31]. The take-off point was determined using a rate of GRF development method previously described [26; 32]. Signal processing of IMU data was performed using a custom MATLAB(r) script (The MathWorks Inc., Natick, MA, USA). For each IMU, angular velocity and acceleration data were fused using an error-state Kalman filter [13], which provided an estimate of vertical acceleration aligned with the global reference frame [26]. Once the acceleration signals were aligned to the global vertical axis, they were resampled to 1000 Hz and filtered using a 4 th-order recursive low-pass Butterworth filter with a cut-off frequency of 50 Hz [26]. Filtered vertical acceleration signals from the IMUs were combined using segment-mass weighting coefficients adapted from [PERSON] (Tables 2-5) [27]. The vertical acceleration signals from the 4-segment IMU model (trunk, thighs, shanks, and feet) representing the WBCM were aligned with vertical acceleration signals obtained from the force platform through cross-correlation performed in MATLAB [26]. After signal alignment, the vertical acceleration signals from the IMUs were combined to create 1-, 2-, 3-, and 4-segmentIMU models of the WBCoM, using the segment-mass weighting coefficients from [PERSON] (Tables 2-5) [26; 27]. Vertical acceleration signals were subsequently converted to vertical velocity time-series data through single numerical integration, performed using the trapezoidal rule. CMJ take-off velocity (v\({}_{\text{t-off}}\)) was defined as the vertical velocity recorded at the time point