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Arm Muscular Effort Estimation from Images Using Computer Vision and Machine Learning

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Ambient Intelligence for Health (AmIHEALTH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9456))

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Abstract

A problem of great interest in disciplines like occupational medicine, ergonomics, and sports, is the measurement of biomechanical variables involved in human movement and balance such as internal muscle forces and joint torques. This problem is solved by a two-step process: data capturing using impractical, intrusive and expensive devices that is then used as input in complex models for obtaining the biomechanical variables of interest. In this work we present a first step towards capturing input data through a more automated, non-intrusive and economic process, specifically weight held by an arm subject to isometric contraction as a measure of muscular effort. We do so, by processing RGB images of the arm with computer vision (Local Binary Patterns and Color Histograms) and estimating the effort with machine learning algorithms (SVM and Random Forests). In the best case we obtained an FMeasure \(=70.68\,\%\), an Accuracy \(=71.66\,\%\) and a mean absolute error in 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 random results indicating that, despite the simplicity of the approach, it is possible to extract meaningful information for the predictive task. Moreover, the simplicity of the approach suggests many lines of further improvements: on the image capturing side with other kind of images; on the feature extraction side with more sophisticated algorithms and features; and on the knowledge extraction side with more sophisticated learning algorithms.

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Acknowledgements

We thank CONICET for the funding of the author Leandro Abraham through a doctoral grant under the supervision of Dr. Raymundo Forradellas and the thesis supervision of Dr. Facundo Bromberg.

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Authors and Affiliations

  1. Laboratorio DHARMa, DeSI, Universidad Tecnológica Nacional, FRM, Mendoza, Argentina

    Leandro Abraham & Facundo Bromberg

  2. CEAL, Universidad Nacional de Cuyo, Facultad de Ingeniería, Mendoza, Argentina

    Leandro Abraham & Raymundo Forradellas

  3. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Leandro Abraham & Facundo Bromberg

Authors
  1. Leandro Abraham
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  2. Facundo Bromberg
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  3. Raymundo Forradellas
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Correspondence to Leandro Abraham .

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Editors and Affiliations

  1. Castilla-La Mancha University, CIUDAD REAL, Ciudad Real, Spain

    José Bravo

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Ramón Hervás

  3. Technological University of Panamá, Panama, Panama

    Vladimir Villarreal

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Abraham, L., Bromberg, F., Forradellas, R. (2015). Arm Muscular Effort Estimation from Images Using Computer Vision and Machine Learning. In: Bravo, J., Hervás, R., Villarreal, V. (eds) Ambient Intelligence for Health. AmIHEALTH 2015. Lecture Notes in Computer Science(), vol 9456. Springer, Cham. https://doi.org/10.1007/978-3-319-26508-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-26508-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26507-0

  • Online ISBN: 978-3-319-26508-7

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