Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
| Autor: | Facundo Bromberg, Leandro Abraham, Raymundo Forradellas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Support Vector Machine
020205 medical informatics Computer science Maximum voluntary contraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud Health Informatics 02 engineering and technology INGENIERÍAS Y TECNOLOGÍAS Biceps 03 medical and health sciences 3D POINT CLOUDS 0302 clinical medicine TELE-PHYSIOTHERAPY Arm muscle 0202 electrical engineering electronic engineering information engineering Humans Computer vision BICEPS ACTIVATION ESTIMATION Otras Ingeniería Eléctrica Ingeniería Electrónica e Ingeniería de la Información Muscle Skeletal Ingeniería Eléctrica Ingeniería Electrónica e Ingeniería de la Información business.industry Supervised learning ENSEMBLE OF SHAPE FUNCTIONS Cloud Computing BIOMECHANICS Computer Science Applications Biomechanical Phenomena Support vector machine SUPPORT VECTOR MACHINES Arm RGB color model Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery |
| Popis: | Background: Muscle activation level is currently being captured using impractical 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 perturbations 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 — an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. Conclusions: The results prove that the correlations between the external 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. Fil: Abraham, Leandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo; Argentina |
| Databáze: | OpenAIRE |
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