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Feet Fidgeting Detection Based on Accelerometers Using Decision Tree Learning and Gradient Boosting

Part of the Lecture Notes in Computer Science book series (LNBI,volume 10814)

Abstract

Detection of fidgeting activities is a field which has not been much explored as of now. Studies have shown that fidgeting has a beneficial impact on people’s healthiness as it burns a significant amount of energy. Being able to detect when someone is fidgeting would allow to study more closely the health impact of fidgeting. The purpose of this work is to propose an algorithm being able to detect feet fidgeting period of subjects while sitting using 3-D accelerometers on both shoes. Initial results on data from 5 subjects collected during this work shows an accuracy of 95% for a classification between sitting with fidgeting and sitting without fidgeting.

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Correspondence to Julien Esseiva .

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Esseiva, J., Caon, M., Mugellini, E., Khaled, O.A., Aminian, K. (2018). Feet Fidgeting Detection Based on Accelerometers Using Decision Tree Learning and Gradient Boosting. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-78759-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78758-9

  • Online ISBN: 978-3-319-78759-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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