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Multi-sensor Platform for Automatic Assessment of Physical Activity of Older Adults

  • Andrea CaroppoEmail author
  • Alessandro Leone
  • Pietro Siciliano
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 539)

Abstract

This work presents a multi-sensor platform integrating one or more commercial low-cost ambient sensors and one wearable device for the automatic assessment of the physical activity and sedentary time of an aged person. Each sensor node could operate in a stand-alone way or in a multi-sensor approach; in the last case, fuzzy logic data fusion techniques are implemented in a gateway in order to improve the robustness of the estimation of a physiological measure characterizing the level of physical activity and specific parameters for the quantification of a sedentary lifestyle. The automatic assessment was conducted through two main algorithmic steps: (1) recognition of well-defined set of human activities, detected by ambient and wearable sensor nodes, and (2) estimation of a physiological measure, that is (MET)-minutes. The overall accuracy for activity recognition, obtained using simultaneously ambient and wearable sensors data, is about 5% higher of single sub-system and about 2% higher of that obtained with more than one ambient sensor. The effectiveness of the platform is demonstrated by the relative error between IPAQ-SF score (used as ground-truth, in which a low score corresponds to a sedentary lifestyle whereas a high score refers to moderate-to-vigorous activity level) and average measured (MET)-minutes obtained by both sensor technologies (after data fusion step), which never exceeds 7%, thus confirming the advantage of data fusion procedure for different aged people used for validation.

Keywords

Intelligent environments Ambient assisted living Human activity recognition Ambient sensor Wearable sensor 

References

  1. 1.
    He, W., Goodkind, D., Kowal, P.: An aging world: 2015. US Census Bureau, pp. 1–165 (2016)Google Scholar
  2. 2.
    Bassett Jr., D.R., Wyatt, H.R., Thompson, H., Peters, J.C., Hill, J.O.: Pedometer-measured physical activity and health behaviors in United States adults. Med. Sci. Sports Exerc. 42(10), 1819 (2010)CrossRefGoogle Scholar
  3. 3.
    Lee, M., Kim, J., Jee, S.H., Yoo, S.K.: Review of daily physical activity monitoring system based on single triaxial accelerometer and portable data measurement unit. In: Machine Learning and Systems Engineering, pp. 569–580. Springer Netherlands (2010)Google Scholar
  4. 4.
    Dwyer, T.J., Alison, J.A., Mc Keough, Z.J., Elkins, M.R., Bye, P.T.P.: Evaluation of the SenseWear activity monitor during exercise in cystic fibrosis and in health. Respir. Med. 103(10), 1511–1517 (2009)CrossRefGoogle Scholar
  5. 5.
    Unick, J.L., Lang, W., Tate, D.F., Bond, D.S., Espeland, M.A., Wing, R.R.: Objective estimates of physical activity and sedentary time among young adults. J. Obes. 2017 (2017)CrossRefGoogle Scholar
  6. 6.
    Paffenbarger, R., Wing, A., Hyde, R.: Paffenbarger physical activity questionnaire. Am. J. Epidemiol. 108, 161–175 (1978)CrossRefGoogle Scholar
  7. 7.
    Claridge, E.A., McPhee, P.G., Timmons, B.W., Martin, G.K., MacDonald, M.J., Gorter, J.W.: Quantification of physical activity and sedentary time in adults with cerebral palsy. Med. Sci. Sports Exerc. 47(8), 1719–1726 (2015)CrossRefGoogle Scholar
  8. 8.
    Kellokumpu, V., Pietikäinen, M., Heikkilä, J.: Human activity recognition using sequences of postures. In: MVA, pp. 570–573 (2005)Google Scholar
  9. 9.
  10. 10.
    Diraco, G., Leone, A., Siciliano, P.: Geodesic-based human posture analysis by using a single 3D TOF camera. In: Proceedings of ISIE, pp. 1329–1334 (2011)Google Scholar
  11. 11.
    Smartex Wearable Wellness System (WWS). http://www.smartex.it/en/our-products/232-wearable-wellness-system-wws. Accessed 14 Mar 2018
  12. 12.
    He, Y., Li, Y.: Physical activity recognition utilizing the built-in kinematic sensors of a smartphone. Int. J. Distrib. Sens. Netw. 9(4), 481580 (2013)CrossRefGoogle Scholar
  13. 13.
    Craig, C.L., Marshall, A.L., Sjorstrom, M., Bauman, A.E., Booth, M.L., Ainsworth, B.E., Pratt, M., Ekelund, U., Yngve, A., Sallis, J.F., Oja, P.: International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35(8), 1381–1395 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Research Council of ItalyInstitute for Microelectronics and MicrosystemsLecceItaly

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