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

  • Julien Esseiva
  • Maurizio Caon
  • Elena Mugellini
  • Omar Abou Khaled
  • Kamiar Aminian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Fidgeting detection Decision tree Boosting Accelerometers Footwear Wearable Machine learning 

References

  1. 1.
    Lugade, V., Fortune, E., Morrow, M., Kaufman, K.: Validity of using tri-axial accelerometers to measure human movement—Part I: posture and movement detection. Med. Eng. Phys. 36(2), 169–176 (2014)CrossRefGoogle Scholar
  2. 2.
    Zhang, T., Tang, W., Sazonov, E.S.: Classification of posture and activities by using decision trees. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4353–4356. IEEE, August 2012Google Scholar
  3. 3.
    Ayumi, V.: Pose-based human action recognition with Extreme Gradient Boosting. In: 2016 IEEE Student Conference on Research and Development (SCOReD), pp. 1–5. IEEE, December 2016Google Scholar
  4. 4.
    Zhang, T., Klein, D.A., Walsh, T., Lu, J., Sazonov, E.S.: Android TWEETY—a wireless activity monitoring and biofeedback system designed for people with Anorexia Nervosa. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5. IEEE, June 2014Google Scholar
  5. 5.
    el Achkar, C.M., Lenoble-Hoskovec, C., Paraschiv-Ionescu, A., Major, K., Büla, C., Aminian, K.: Instrumented shoes for activity classification in the elderly. Gait Posture 44, 12–17 (2016)CrossRefGoogle Scholar
  6. 6.
    Munguia Tapia, E.: Using machine learning for real-time activity recognition and estimation of energy expenditure. Doctoral dissertation, Massachusetts Institute of Technology (2008)Google Scholar
  7. 7.
    Moufawad El Achkar, C., Lenoble-Hoskovec, C., Paraschiv-Ionescu, A., Major, K., Büla, C., Aminian, K.: Physical behavior in older persons during daily life: insights from instrumented shoes. Sensors 16(8), 1225 (2016)CrossRefGoogle Scholar
  8. 8.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88(6), 2297–2301 (1991)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Applied Sciences and Arts Western SwitzerlandFribourgSwitzerland
  2. 2.Laboratory of Movement Analysis and MeasurementEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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