## 2 Materials and Methods
### Experimental Protocol  
To compare and validate the GNSS-based estimates against standard references, we employed a 3D camera system, IMUs, and two 2D cameras. Participants performed a maximal 400 m sprint starting from a crouching position using starting blocks in lane 1. Before the sprint, a standardized warm-up lasting approximately one hour was conducted to replicate the athletes' typical pre-race routines and ensure they reached their maximum competition speed. The warm-up included mobility drills, sprint technique exercises, progressively intensified running, and short rest periods.  
Positional data were recorded using a single RTK GNSS receiver system. By attaching the receiver to the runner's head, we ensured both unobstructed satellite reception and a stable proxy for the body's center of mass (CoM) motion [30]. This receiver was mounted onto rugby headgear, with the GNSS unit secured in a waist pack [19].  
IMUs were affixed above each ankle to validate the _SF_ and detect IC events [13]. Since IC events are best detected near the feet, IMUs were placed at the ankles to capture foot-ground contact accurately. Additionally, an IMU was mounted onto the headgear adjacent to the GNSS antenna solely for synchronization purposes. All IMUs were powered on simultaneously using a dedicated remote controller to ensure their internal clocks started nearly simultaneously. For clarity, we define the coordinate axes of each IMU as follows: the \(x\) axis is oriented mediolaterally (left-right), the \(y\) axis is oriented anteroposteriorly (aligned with the running or forward-backward direction), and the \(z\) axis is oriented vertically (aligned with gravity).  
Specifically, we conducted 3D video analysis in two track segments--one curved (286-294 m) and one straight (356-364 m)--to account for biomechanical differences in running on straight versus curved paths [31]. Each segment was recorded by two cameras positioned at approximately 30\({}^{\circ}\) and 60\({}^{\circ}\) angles relative to the running lane, situated outside the runway behind the runner. These cameras captured an 8-m section spanning the full lane width which was 2 m in height. An 18-point calibration check ensured accurate spatial measurements for the \(SL\), \(SF\), and _running velocity_. To synchronize the GNSS, IMUs, and video data, the participants briefly stood still (1-2 s) in an upright posture before performing a short vertical jump (15-25 cm) in front of each 3D camera--once for the curved section and once for the straight section--immediately before getting into the starting blocks. This approach of using a short vertical jump for synchronization has been adopted in prior studies [32]. During the brief static posture, we estimated the initial orientation of the head-mounted IMU and removed the gravitational acceleration component from the head IMU signals, referencing previous work [33]. Consequently, the head IMU data were transformed into an absolute coordinate system before the jump. The jump created a distinct waveform in the head-mounted IMU's vertical axis, the GNSS-derived vertical displacement, and the camera-digitized vertical position of the GNSS antenna. We achieved sub-frame synchronization of the timing data by aligning these characteristic waveforms across all devices during post-processing. A similar event-based synchronization strategy has been reported for multiple wearable systems [34].  
Additionally, a 2D camera at the start and finish line recorded the total sprint time, and a bicycle-mounted 2D camera, kept about 10 m behind the runner, verified the total step count throughout the run.