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Machine Learning Modeling of Human Activity Using PPG Signals

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Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12496))

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Abstract

The use of wearables is contributing towards the decrease of risk for chronic diseases related to cardiovascular or diabetes problems. Most wearables measure heart rate and the majority of them uses a Photoplethysmography (PPG) sensor. A serious limitation of the PPG sensors is their sensitivity to Motion Artifacts (MAs) which can severely corrupt the raw signal. Accurate estimation of the PPG signal as it is recorded from the subject’s wearables while performing various physical activities, is a challenging task. This research introduces a novel Human Activity Recognition (HAR) approach that determines the subject’s activity, by considering the respective PPG signal. It considers the public PPG-DaLiA dataset, for 15 persons, related to 9 activities. Totally, 24 Machine-Learning (ML) techniques were used. The weighted k-Nearest Neighbors (k-NN), the Cubic Support Vector Machines C-SVM and the Bagged Trees (BGT) have achieved the best performance.

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Authors and Affiliations

  1. Department of Civil Engineering, Democritus University of Thrace, 67100, Xanthi, Greece

    Anastasios Panagiotis Psathas, Antonios Papaleonidas & Lazaros Iliadis

Authors
  1. Anastasios Panagiotis Psathas
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  2. Antonios Papaleonidas
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  3. Lazaros Iliadis
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Corresponding author

Correspondence to Antonios Papaleonidas .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Thua Thien Hue Center of Information Technology, Hue, Vietnam

    Bao Hung Hoang

  3. Vietnam - Korea University of Information and Communication Technology, University of Da Nang, Da Nang, Vietnam

    Cong Phap Huynh

  4. Department of Computer Engineering, Yeungnam University, Gyeungsan, Korea (Republic of)

    Dosam Hwang

  5. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

  6. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Psathas, A.P., Papaleonidas, A., Iliadis, L. (2020). Machine Learning Modeling of Human Activity Using PPG Signals. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_42

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  • DOI: https://doi.org/10.1007/978-3-030-63007-2_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63006-5

  • Online ISBN: 978-3-030-63007-2

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