A problem of great interest in biomechanics is the estimation of internal muscle forces and joint torques. Direct measurement of these variables is not possible, so it is addressed in practice using mechanical models that takes as input kinematics and muscle activity. Input data for these models is captured using impractical, intrusive and expensive devices. In this work we present firsts steps towards capturing muscle activity of the arm biceps through a less intrusive and more economic process. First, we consider an isometric contraction setup for which muscle activity directly correlates with external weight supported by the muscle. Then, using Computer Vision feature extraction algorithms (Local Binary Patterns and Color Histograms) and Machine Learning algorithms we learn a classifier (e.g., Support Vector Machines and Random Forests) for inferring the weight held by the muscle. We consider two similar problems: discriminating between two weights and discriminating between four target weights. Also, we consider the use of calibration information to better generalize over unseen subjects. We obtained promising results showing accuracies of $79.78\%$ and $42.34\%$ for the cases with calibration of two and four levels respectively. The main difficulty of this approach is to generalize the learned model over unseen subjects, due to the great differences existing among human subjects arms. Considering the difficulty in the requirement of generalization, and the simplicity of our approach, it is enlightening to achieve over random results, suggesting that it is possible to extract meaningful information for the predictive task.
['Leandro Abraham', 'Facundo Bromberg', 'Raymundo Forradellas']
Arm Muscular Effort Estimation from Images Using Computer Vision and Machine Learning