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

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Sensors (CNS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 539))

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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.

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Authors and Affiliations

  1. National Research Council of Italy, Institute for Microelectronics and Microsystems, Lecce, Italy

    Andrea Caroppo, Alessandro Leone & Pietro Siciliano

Authors
  1. Andrea Caroppo
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  2. Alessandro Leone
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  3. Pietro Siciliano
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Corresponding author

Correspondence to Andrea Caroppo .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Bruno Andò

  2. IFAC-CNR, Sesto Fiorentino, Florence, Italy

    Francesco Baldini

  3. University of Rome Tor Vergata, Rome, Italy

    Corrado Di Natale

  4. Department of Information Engineering (DII), University of Brescia, Brescia, Italy

    Vittorio Ferrari

  5. University of Catania, Catania, Italy

    Vincenzo Marletta

  6. Department of Chemistry, University of Florence, Sesto Fiorentino, Florence, Italy

    Giovanna Marrazza

  7. University of Palermo, Palermo, Italy

    Valeria Militello

  8. University of Padova, Padua, Italy

    Giorgia Miolo

  9. Sapienza University of Rome, Rome, Italy

    Marco Rossi

  10. Università Politecnica delle Marche (UNIVPM), Ancona, Italy

    Lorenzo Scalise

  11. IMM-CNR, Lecce, Italy

    Pietro Siciliano

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Caroppo, A., Leone, A., Siciliano, P. (2019). Multi-sensor Platform for Automatic Assessment of Physical Activity of Older Adults. In: Andò, B., et al. Sensors. CNS 2018. Lecture Notes in Electrical Engineering, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-04324-7_51

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  • DOI: https://doi.org/10.1007/978-3-030-04324-7_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04323-0

  • Online ISBN: 978-3-030-04324-7

  • eBook Packages: EngineeringEngineering (R0)

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