## 4 Discussion
(2) Using the subjects' meta-data dataset, as [8] included a dataset that provides for more information regarding the persons, such as weight and height, which could be used to adjust the force prediction for each subject, as the force not only depends on acceleration, but also on mass, which is not considered on the current model. This could be used when using an architecture of two neural networks, the first consisting of an RNN that makes predictions for each force (\(F_{x}\), \(F_{y}\), \(F_{z}\)), and the second an ANN that takes as input both the predicted forces and the subject's meta-data to adjust the predictions. (3) Increasing the number of epochs, as in this work, \(n_{epoch}=25\) was used to find the best combination of parameters to avoid spending longer than 30 min on a model's training; however, increasing the number of epochs would most likely increase the model's performance as it would learn better on the training data; although, if not evaluated correctly, this could lead to over-fitting or under-fitting by the model, but the training/validation curves in Figure 7 seem to show an ideal fit trend as the number of epochs increases. (4) Trying other ML algorithms [29] or DL architectures [30]. CNN [31], which is usually used to analyze images, could also be used in time-series analysis [32]. The current work presented a quick grid search that used a one-layer RNN with \(n_{epoch}~{}=~{}25\) to determine which model performed best, according to a grid search that iterated over the combination of a given set of parameters (loss function, type of layer, sampling technique). The model's predictions could be improved when considering more complex DL architectures, such as the 4-layered DL models used in [32], which combined CNN, RNN, and ANN layers to achieve a 99% accuracy fall-risk prediction using a force plate. However, the model had 362, 243 parameters, as opposed to our 1-layer RNN with LSTM using 3099 parameters; it also used force, moment, and center of pressure (CoP) measurements, whereas our model only used markers positions without further processing. On the other hand, other studies used 1-layered ANNs biomechanical features, such as [23], which used GRF, vertical displacement, velocity of the center of mass, and jump power to predict joint torque. The correlation coefficient was 0.967, making it efficient but not very practical, as data have to be processed and converted to biomechanical features. In contrast, [21] used a similar approach concerning the current work on using raw XYZ positions and body weight to predict joint angles and accelerations, reaching an \(R^{2}>0.95\). The presented work can be used to predict in real-time forces generated at specific joints with given video; and can be applied to applications in ergonomy [33], posture correction, safety in the workplace [34], training, sports performance, and injury prevention [14], among others. The proposed framework can be implemented easily with open-source programming tools (e.g., Python); however, knowledge of deep learning and biomechanics is needed. Therefore, in order to provide insight into athletes' performance, it would be ideal to use the support of a biomechanical analyst to act as a mediator for a sports-biomechanics translation [35]. Furthermore, to allow a more user-friendly approach, the method could be embedded into a mobile application so that athletes and coaches can use it in a more direct manner, similarly concept of commercial applications such as Kinovea [36] and Dartfish [37].