Biomechanical studies are essential in health research areas, such as rehabilitation, kinesiology, orthopedics, and sports. For example, they provide information to elaborate on patients’ diagnostics or improve athletes’ performance. In recent years, deep learning and other computational methods have started to be used to quantify new biomechanical parameters or perform deeper data analysis. Motion capture is one of the methods commonly used in biomechanical studies. For this method, video-based and marker-based systems are the gold standards; nevertheless, those systems are typically quite expensive. Moreover, experimental errors in data capture are frequently related to the occlusion of the markers during motion capture. Data missed is solved by increasing the number of cameras to cover more angles or by using predetermined interpolation algorithms. However, the last method could fail to predict all the marker data missed, and both options increase the cost of the data analysis. For solving those kinds of problems, novel computational methods could be used. This study aims to implement an artificial neural network (ANN) to estimate the limb angle amplitude during the execution of a movement from a single axis (X-axis). For training and validating the ANN model, the data and features from the Five-Minute Shaper machine (a physical conditioning device) are used. The obtained results include RMSE values smaller than 3.2 (Minimum RMSE of 0.96) and CC values close to 0.99. The predicted values are very close to the experimental amplitude angles, and, according to the Two-sample Kolmogorov-Smirnov test, the experimental and the estimated amplitude angles follow the same continuous distribution (p- valueygt; 0.05 ). It is shown that these methods could help researchers in biomechanics to perform accurate analysis, reducing the number of needed cameras and avoid problems due to occlusion by only needing information from a specific axis.
