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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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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