## 5 Conclusions  
In this work, the use of RNN structures in the context of sports biomechanics was successful, as the comparisons between the original and the predicted forces were satisfactory, as observed in Figure 8. While investigating the RNN training performance using different architectures, the \(t_{epoch}\) and \(t_{elapsed}\) parameters allowed us to confirm that simple layering (in this application) was less efficient than GRU and LSTM. In our case, the more complex architectures were the ones that generated the best results. Rather than proposing a unique solution, in this work, many parameters were explored to obtain the best model. For example, performance was measured using metrics such as the Pearson correlation coefficient (\(r\)) and coefficient of determination (\(R^{2}\)). Additionally, combinations of modeling parameters, such as sampling methods (up-sampling, down-sampling), type of layer (GRU, LSTM, Simple), and loss function (MAE, MSE, MSLE), were added to the evaluation of the models to find the best possible model. Considering all the work presented in this manuscript, it is clear that the proposed framework was successful at correctly predicting the forces generated on a force platform, using positions from markers. This is a significant result in sports biomechanics because it means that using similar methods, applications can be built to predict real-time forces generated in athletes' joints, and coaches could use such predictions for injury prevention, technique assessment, and personalized coaching, among others. Future work will include the implementation of the proposed framework, but using data obtained from more realistic scenarios (e.g., using wearables and cameras) instead of using information from a database. The real-world proposal would also be tested using a similar approach to the one presented in order to find the best model and then test it in real-time (where upper limit constraints on prediction times are essential to take into account due to the dynamics of the real-time application). Finally, it is essential to remark that this work has increased our understanding of automatic force prediction in human movements and may help develop new clinical approaches to avoid the development of fatigue fractures and implementation of proper physical training regimes. Conceptualization, J.J.L.-S., M.A.R.-M., G.P.-E., and M.O.C.-L.; methodology, M.A.R.-M., A.S.-R., and M.O.C.-L.; software, M.O.C.-L.; validation, M.A.R.-M., A.S.-R., and M.O.C.-L.; formal analysis, M.O.C.-L.; investigation, M.A.R.-M., E.A.G.-F., and M.O.C.-L.; resources, R.A.R.-M.; writing--original draft preparation, M.A.R.-M., E.A.G.-F., and M.O.C.-L.; writing--review and editing, [PERSON]-S., M.A.R.-M., and [PERSON]-E.; visualization, [PERSON]-S., M.A.R.-M., and [PERSON]-E.; supervision, [PERSON]-S., M.A.R.-M., A.S.-R., and [PERSON]-E.; project administration, [PERSON]-S., M.A.R.-M., and [PERSON]-E.; funding acquisition, R.A.R.-M. All authors have read and agreed to the published version of the manuscript. This research was funded by Tecnologico de Monterrey, and the Campus City initiative. Ethical review and approval were waived for this study because no experimental procedures with humans were carried out. The data analyzed in this work came from a publicly available dataset described in [8], and the information regarding the ethical committee approval of the study are in there. Patient consent was waived for this study because no experimental procedures with humans were carried out. The data used in this work are available in [8]. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: