## 4 Discussion
Based on our hypothesis, a 3-segment model comprising the trunk, thighs, and shanks was expected to perform best, as these segments have the greatest proportional mass. However, this was not observed. Instead, a 3-segment model including the trunk, thighs, and feet outperformed the model with the trunk, thighs, and shanks, suggesting a similar rationale applies to this unexpected outcome. For example, including the shanks in the model reduced the trunk's weighting to 52.07% for females and 54.03% for males, whereas including the feet increased the trunk's weighting to 56.98% for females and 58.32% for males. Although the feet are minimally involved during most of the CMJ cycle, including the shanks, they appear to introduce greater error into the model by reducing the relative weighting of the trunk and, likely, the thighs as well. This rationale may also explain why the best-performing 2-segment model included the trunk and shanks, contrary to the expectation that the trunk and thighs would yield better performance. The IMU models with the lowest RMSE also exhibited the smallest systematic bias for CMJ JH. For instance, the systematic bias for the best-performing 2- and 3-segment models was \(-0.1\) cm and \(0.3\) cm, respectively, while the bias for the 4-segment and best-performing 1-segment models was \(1.5\) cm and \(2.5\) cm, respectively. However, it is important to note that no statistically significant differences were observed between any of the models when compared to the force platform criterion. Although the relatively small systematic bias observed in this investigation is consistent with biases reported in recent research using the flight time method [20; 21; 22; 33], systematic bias alone does not fully reflect validity. For example, the 2-segment model, including the trunk and shanks, yielded the lowest systematic bias (\(-0.1\) cm). However, when simple linear regression was performed, the coefficient of determination (R\({}^{2}\) = 0.68, Figure 1) indicated that the IMU model contains random error not fully explained by the systematic bias. When RMSE is expressed as a percentage of the average vertical acceleration, the top-performing IMU models showed RMSE values between 15.6% and 23.3% of the average acceleration measured by the force platform criterion (Table 9). These errors are larger than those observed in recent studies that used single-IMU configurations and machine learning algorithms to estimate biomechanical measures in CMJ and depth jumps (DJ). For example, [PERSON] et al. [24] reported a 5.1% error in measuring peak power during CMJ using a single-IMU setup and machine learning compared to a force platform criterion. Similarly, [PERSON] et al. [25] found errors ranging from 4.6% to 11.4% when measuring peak GRF, peak knee flexion, peak knee power absorption, and peak internal knee moment with a single-IMU configuration and machine learning, compared to force platform and optical motion capture criteria. There is potential to apply advanced machine learning techniques to optimize segment weights, which could enhance the validity of vertical acceleration IMU data and, in turn, improve the accuracy of CMJ JH estimation featuring multiple-IMU configurations, yet this represents a potential focus for future investigation. While enhancing the validity of IMU assessment of CMJ performance metrics remains a key focus in the research literature, a single IMU is likely insufficient to capture the mechanics acting through the WBCoM. In the current investigation, single-IMU models placed on segments with lower mass relative to the whole body resulted in higher RMSE values and larger overestimations of CMJ JH. The trunk was included in all of the best-performing IMU models; however, when the trunk IMU data were analyzed independently, it produced a roughly 50% increase in RMSE compared to the best 2- and 3-segment models, along with the largest systemic bias in CMJ JH estimation relative to all other best-performing models.