Implementation of computational methods to estimate lower limb angle amplitudes during squat
Abstract
In biomechanics, motion capture systems based on video and markers are the most widely used method to estimate kinematic parameters. However, from a technical standpoint, experimental errors in data capture are often related to the masking of markers during motion capture. This phenomenon generates data loss that can affect the analysis of the results. The lack of data is solved by increasing the number of cameras or using additional devices such as inertial sensors. However, those additions increase the experimental cost of this method. Nowadays, new computational methods can be used to solve such problems less expensively. This study implemented two computational methods based on Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) to estimate the amplitude of limb angles during the execution of a movement on a single axis (i.e., the z-axis). The characteristics of the squats were used to train and validate the models. The results obtained include RMSE values lower than 14 (minimum RMSE of 5.35) and CC values close to 0.98. The estimated values are very close to the experimental amplitude angles, and the statistical analyses showed no significant differences between the distributions and means of the estimated amplitude values and their actual counterparts (p-value>0.05). The results show that these methods could help biomechanics researchers perform accurate analyses, decrease the number of cameras needed, reduce uncertainty, and avoid data loss problems.
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