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Human Activity Recognition Using Accelerometer and Photoplethysmographic Signals

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 73))

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Abstract

This paper presents an efficient technique for real-time recognition of human activities by using accelerometer and photoplethysmography (PPG) data. It is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition. Due to the nature of signals, and being the algorithm independent from the orientation of the inertial sensor, this technique is particularly suitable for implementation in smartwatches in order to both recognize the exercise being performed and improve the motion artifact (MA) removal from PPG signal for accurate heart rate (HR) estimation. In order to demonstrate the validity of this methodology, it has been successfully applied to a database of accelerometer and PPG data derived from four dynamic activities.

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Authors and Affiliations

  1. DII – Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy

    Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simone Orcioni & Claudio Turchetti

Authors
  1. Giorgio Biagetti
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  2. Paolo Crippa
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  3. Laura Falaschetti
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  4. Simone Orcioni
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  5. Claudio Turchetti
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Corresponding author

Correspondence to Paolo Crippa .

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Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C. (2018). Human Activity Recognition Using Accelerometer and Photoplethysmographic Signals. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-59424-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59423-1

  • Online ISBN: 978-3-319-59424-8

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