## 5 Conclusions  
This study investigated the effects of IMU sensor location and number on the validity of vertical acceleration measurements and CMJ JH estimation. Findings revealed that IMU models incorporating acceleration data from body segments with larger proportional masses provided more accurate CMJ metrics, supporting the hypothesis to some extent. The best-performing models included combinations of the trunk with other segments, such as the shanks or feet, emphasizing the importance of trunk data in optimizing IMU model accuracy. However, contrary to expectations, the 4-segment model did not significantly outperform simpler models, likely due to reduced trunk weighting caused by including less active segments, such as the feet. Systematic bias and RMSE analyses further highlighted the strengths and limitations of IMU-based models. While the best-performing 2- and 3-segment models exhibited the smallest biases (\(-0.1\) cm and \(0.3\) cm, respectively) and the lowest RMSE values, the overall accuracy was constrained by random errors inherent to the IMU data. Single-IMU models, particularly when placed on lower-mass segments, demonstrated greater RMSE and overestimations of CMJ JH, reinforcing the insufficiency of single-sensor approaches for capturing the WBCoM mechanics during complex movements.  
For practitioners, this study suggests a balance between accuracy and practicality when selecting IMU placements for CMJ analysis. Models incorporating trunk data, alongside key body segments like the shanks or feet, can optimize accuracy while maintaining feasibility in a training environment. However, simpler configurations like 2- and 3-segment models may provide a practical compromise, offering reasonable accuracy with less complexity. Future research should investigate integrating IMU data with other wearable technologies, such as global positioning system (GPS) sensors, to enhance motion analysis. This could provide a more comprehensive understanding of movement mechanics and improve the reliability of IMU-based assessments in dynamic, real-world training scenarios. Continued refinement of sensor configurations and modeling techniques will be crucial for advancing the application of IMU technology in both biomechanical research and practical training settings.  
Conceptualization, D.A., C.W., B.J.T. and T.L.; methodology, D.A. and T.L.; software, J.P. and T.L.; validation, T.L.; formal analysis, D.A., J.P. and T.L.; investigation, D.A., C.W., S.B.S., J.P., B.J.T. and T.L.; resources, B.J.T. and T.L.; data curation, D.A., J.P. and T.L.; writing--original draft preparation, D.A., C.W., B.J.T. and T.L.; writing--review and editing, D.A., C.W., S.B.S., J.P., B.J.T. and T.L.; visualization, T.L.; supervision, B.J.T. and T.L.; project administration, B.J.T. and T.L.; funding acquisition, B.J.T. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted according to the ethical principles stated in the Declaration of Helsinki and approved by the Utah State University Institutional Review Board (#11903 from 4 April 2021).  
All participants provided informed consent form prior to enrollment.  
The raw data supporting the conclusion of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.