@article {219, title = {Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds}, journal = {Computers in Biology and Medicine}, volume = {95}, year = {2018}, month = {04/2018}, pages = {129-139}, chapter = {129}, abstract = {

Background: Muscle activation level is currently being captured using im- practical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non- invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras.

Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturba- tions in the point of view of the capturing device, greatly simplifying the installation process for end-users.

Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g),\ \ the best variant of the proposed methodology achieved mean absolute errors of about 9.21 \% MVC {\textemdash} an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical.

Conclusions: The results prove that the correlations between the exter- nal geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.\ 

}, keywords = {3d point clouds, biceps activation estimation, biomechanics, ensemble of shape functions, support vector machines, Tele-physiotherapy}, issn = {0010-4825}, doi = {https://doi.org/10.1016/j.compbiomed.2018.02.011}, url = {https://www.sciencedirect.com/science/article/pii/S0010482518300416}, author = {Leandro Abraham and Facundo Bromberg and Raymundo Forradellas} }