@conference {168,
	title = {Arm muscular effort estimation from images using Computer Vision and Machine Learning},
	booktitle = {1st International Conference on Ambient Intelligence for Health},
	year = {2015},
	month = {Dec},
	address = {Puerto Varas, Chile},
	abstract = {<p>A problem of great interest in disciplines like occupational\&nbsp;medicine, ergonomics, and sports, is the measurement of biomechanical\&nbsp;variables involved in human movement and balance such as internal\&nbsp;muscle forces and joint torques. This problem \&nbsp;is solved by a two-step process: data capturing using impractical, intrusive and expensive devices\&nbsp;that is then used as input in \&nbsp;complex models for obtaining the biomechanical variables of interest. In this work we present a first step towards\&nbsp;capturing input \&nbsp;data through a more automated, non-intrusive and economic process, specifically weight held by an arm subject to isometric\&nbsp;contraction as a measure of muscular effort. We do so, by processing\&nbsp;RGB images of the arm with computer vision (Local Binary Patterns\&nbsp;and Color Histograms) and estimating the effort with machine learning\&nbsp;algorithms (SVM and Random \&nbsp;Forests). In the best case we obtained an\&nbsp;FMeasure= 70.68\%, an Accuracy= 71.66\% and a mean absolute error in\&nbsp;the predicted weights of 554.16 grs (over 3 possible levels of effort). Considering the difficulty of the problem , it is enlightening to achieve over\&nbsp;random results indicating that, despite the simplicity of the approach,\&nbsp;it is possible to extract meaningful information for the \&nbsp;predictive task.\&nbsp;Moreover, the simplicity of the approach suggests many lines of further\&nbsp;improvements: on the image capturing \&nbsp;side with other kind of images;\&nbsp;on the feature extraction side with more sophisticated algorithms and\&nbsp;features; and on the\&nbsp;knowledge extraction side with more sophisticated\&nbsp;learning algorithms.\&nbsp;</p>
},
	keywords = {color histograms, LBP, muscle arm effort, random forests, SVM},
	author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo}
}
