## 4 Discussion  
This study aimed to evaluate the effects of IMU sensor location and number on the validity of vertical acceleration and CMJ JH estimates. A total of 15 unique IMU models were developed based on [PERSON]'s [27] anthropometric data. Using force platform acceleration as the criterion reference, it was hypothesized that IMU models incorporating acceleration data from body segments representing a larger proportion of total body mass (e.g., multiple segments or segments with greater mass proportions) would provide the most accurate estimates of CMJ vertical acceleration and JH. The findings of this study partially supported this hypothesis. However, the practical implications of these findings are influenced by random error in IMU data, which can lead to inconsistent or misleading real-time feedback, hindering an athlete's ability to adjust technique accurately. In precise sports, such errors may distort key metrics, affecting performance analysis and long-term tracking. To reduce these effects, techniques such as data filtering, using multiple IMUs, and proper calibration can improve accuracy and ensure more reliable feedback during training. It was hypothesized that the 4-segment IMU model, constructed from acceleration data corresponding to the approximate segmental centers of mass of the trunk, thighs, shanks, and feet, would produce the lowest RMSE values and the smallest systematic CMJ JH bias. However, this expectation was not met. In the current investigation, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Model** & **RMSE (m \(\times\) s\({}^{-2}\))** \\ \hline Trunks, Thighs, Shanks, & 1 & 2.2 (1.1) \\ Trunk, Thighs, Feet & 13 & 2.0 (1.2) 1 \\ Trunk, Shanks & 7 & 2.1 (1.3) 1 \\ Trunk & 2 & 3.0 (1.4) \\ \hline \hline \end{tabular} Root-mean-square error (RMSE) values were estimated from the best-performing IMU models among 15 different configurations. RMSE was calculated by comparing IMU-derived vertical acceleration during countermovement jumps against the force platform criterion. 1 Significantly different from 1-segment model (_p_ < 0.05). RMSE = root-mean-squared error. Data are presented as mean (SD).  
\end{table} 
Table 7: RMSE ANOVA. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Type** & **Model** & **JH (cm)** \\ \hline Trunks, Thighs, Shanks, & 1 & 30.0 (8.3) \\ Feat & 13 & 28.8 (8.0) \\ Trunk, Shanks & 7 & 28.4 (7.7) \\ Trunk & 2 & 26.0 (7.3) \\ FP & - & 28.5 (8.4) \\ \hline \hline \end{tabular} JH values were estimated from the best-performing IMU models among 15 different configurations. JH was measured during the performance of countermovement jumping using the take-off velocity method for all IMU configurations and the FP criterion. No significant statistical differences were observed (_p_ < 0.05). CMJ = countermovement jump; JH = jump height; FP = force platform criterion. Data are presented as mean (SD).  
\end{table} 
Table 8: CMJ JH ANOVA. RMSE values for the best-performing 2-segment (trunk, shanks) and 3-segment (trunk, thighs, feet) IMU models were significantly lower than those of the 1-segment model, while the 4-segment model showed no significant improvement over the 1-segment model (see Table 7). Nonetheless, there were no significant differences in RMSE values between the 4-segment model and the best-performing 2- and 3-segment models. This can be attributed to the relative mass weightings applied in the different IMU models. For example, in the 4-segment model, including the feet reduced the trunk's relative weighting to 50.48% for females and 52.25% for males. In contrast, the best-performing 3-segment model assigned higher trunk weightings of 56.98% for females and 58.32% for males. Since the feet remain largely inactive until the latter part of the propulsive phase of jumping, including them in a 4-segment model may introduce additional error by reducing the relative weighting of body segments that play a more significant role throughout the entire CMJ.