## 3 Results
### The 400 m Sprint Analysis Results  
In the 400 m sprint analysis, Subject A experienced approximately 50% of the data in 'Float' mode, whereas Subject B maintained a 'Fix' solution for the entire duration. Both GNSS-based methods accurately captured all steps, matching 100% with the reference step counts obtained from the bicycle-mounted camera. Subject A completed 200 steps, and Subject B completed 242 steps. The total sprint times measured by the 2D camera were 55.06 s for Subject A and 73.07 s for Subject B. The GNSS-derived times were 55.090 s for Subject A and 73.065 s for Subject B, showing a 99.95% match for Subject A and 99.99% match for Subject B with the camera-based measurements. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Method** & _RMSE_ & _bias_ & **SD** & **ICC [95\% CI]** & \(R\) \\ \hline Step Frequency & Method 1 & 0.291 & 0.061 & 0.292 & 0.813 [0.701, 0.926] & 0.909 ** \\
[Hz] & Method 2 & 0.205 & 0.025 & 0.208 & 0.886 [0.817, 0.955] & 0.922 ** \\ \hline Step Length & Method 1 & 0.190 & \(-\)0.027 & 0.194 & 0.615 [0.349, 0.882] & 0.681 ** \\
[m] & Method 2 & 0.143 & \(-\)0.006 & 0.148 & 0.755 [0.585, 0.925] & 0.799 ** \\ \hline Running Velocity & Method 1 & 0.308 & \(-\)0.094 & 0.303 & 0.951 [0.917, 0.985] & 0.959 ** \\
[m s\({}^{-1}\)] & Method 2 & 0.344 & 0.010 & 0.355 & 0.950 [0.916, 0.985] & 0.949 ** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical comparison of GNSS-based methods with camera-based measurements. Note: \(n=20\) for _SF_; \(n=16\) for _SL_ and _running velocity_. A double asterisk (**) denotes statistical significance at \(p<0.01\). The two GNSS-based methods were evaluated for accuracy in _SF_ estimation during the full 400 m sprint, using the IMU-derived _SF_ as the reference. Across the trials, the _MAE_ and SD for the IMU-based IC detection were 0.005 \(\pm\) 0.003 s compared with the camera-based reference (\(n\) = 24), confirming the validity of our method. The results were further analyzed for differences between the subjects, legs (left versus right), and track sections (straight versus curved). Table 2 summarizes the results, including both overall and leg-specific comparisons. For Subject A, Method 2 demonstrated improved accuracy and reliability compared with Method 1, showing a lower _RMSE_ (0.232 Hz versus 0.397 Hz), smaller _bias_ (0.016 Hz versus 0.058 Hz), and higher ICC (0.711 versus 0.569). When analyzing the left and right legs separately, Method 2 maintained better consistency, with reduced errors and smaller variability. In contrast, Method 1 exhibited larger biases, particularly for the right leg. For Subject B, Method 2 similarly outperformed Method 1, with an _RMSE_ of 0.197 Hz compared with 0.219 Hz, a smaller _bias_ of 0.017 Hz (versus 0.022 Hz), and a higher ICC of 0.833 compared with 0.809. Method 2 also demonstrated consistent performance for both the left and right legs, whereas Method 1 displayed slightly larger errors and variability in the left leg. Figure 4A-D further illustrates the relationships and agreement between the GNSS-based methods and IMU-derived _SF_ through scatter plots and Bland-Altman analyses. Method 2 showed stronger alignment with the reference values, with smaller differences and narrower LOAs across both subjects and legs. Further analysis compared the _SF_ estimates in straight and curved track sections across the two GNSS-based methods. The statistical metrics summarized in Table 3 show that Method 2 consistently outperformed Method 1 in terms of accuracy and reliability across both track sections and subjects. For curved sections, Method 2 achieved a lower _RMSE_ (0.211 Hz versus 0.348 Hz overall) and _bias_ (0.017 Hz versus 0.050 Hz), with higher ICC values (0.835 versus 0.692) compared with Method 1. Similarly, on straight sections,  
Figure 3: Comparison of GNSS-based methods (Method 1 and Method 2) versus camera-based measurements of _SF_, _SL_, and _running velocity_. (**A–C**) Scatter plots with the line of unity. (**D–F**) Bland–Altman plots showing _bias_ and LOA.