@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}
}
@proceedings {177,
	title = {Estimaci{\'o}n de carga muscular mediante im{\'a}genes},
	journal = {Argentinean Symposium of Artificial Intelligence (ASAI) - Jornadas Argentinas de Inform{\'a}tica},
	year = {2014},
	pages = {91--98},
	publisher = {Sociedad Argentina de Inform{\'a}tica},
	address = {Buenos Aires, Argentina},
	abstract = {<p>Un problema de gran inter{\'e}s en disciplinas como la ocupacional, ergon{\'o}mica y deportiva, es la medici{\'o}n de variables biomec{\'a}nicas involucradas en\&nbsp;el movimiento humano (como las fuerzas musculares internas y torque de articulaciones).\&nbsp;Actualmente este problema se resuelve en un proceso de dos pasos.\&nbsp;Primero capturando datos con dispositivos\&nbsp;poco pr{\'a}cticos, intrusivos y costosos.\&nbsp;Luego estos datos son usados como entrada en modelos complejos para obtener\&nbsp;las variables biomec{\'a}nicas como salida. El presente trabajo representa una alternativa automatizada, no intrusiva y econ{\'o}mica al primer paso, proponiendo la\&nbsp;captura de estos datos a trav{\'e}s de im{\'a}genes. En trabajos futuros la idea es automatizar todo el proceso de c{\'a}lculo de esas variables. En este trabajo elegimos un\&nbsp;caso particular de medici{\'o}n de variables biomec{\'a}nicas: el problema de estimar\&nbsp;el nivel discreto de carga muscular que est{\'a}n ejerciendo los m{\'u}sculos de un brazo. Para \&nbsp;estimar a partir de im{\'a}genes est{\'a}ticas del brazo ejerciendo la fuerza de\&nbsp;sostener la carga, el nivel de la misma, realizamos un\&nbsp;proceso de clasificaci{\'o}n.\&nbsp;Nuestro enfoque utiliza Support Vector Machines para clasificaci{\'o}n, combinada\&nbsp;con una etapa de pre-procesamiento que extrae caracter{\'\i}sticas visuales utilizando variadas t{\'e}cnicas (Bag of Keypoints, Local Binary Patterns, Histogramas de\&nbsp;Color, Momentos de Contornos) En los mejores casos (Local Binary Patterns y\&nbsp;Momentos de Contornos) obtenemos medidas de performance en la clasificaci{\'o}n\&nbsp;(Precision, Recall, F-Measure y Accuracy) superiores al 90 \%.</p>
},
	url = {http://43jaiio.sadio.org.ar/proceedings/ASAI/search.html},
	author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo}
}
