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김성필

Kim, Sung-Phil
Brain-Computer Interface Lab.
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Classification of Physical Activities Based on Photoplethysmography Signals from a Wearable Device

Author(s)
Park, Jong WooKim, JongsuKim, Sung-Phil
Issued Date
2017-12-06
URI
https://scholarworks.unist.ac.kr/handle/201301/38869
Citation
U-Healthcare 2017
Abstract
Recent advances in wearable technology have enabled the users to monitor their physical and physiological states from sensors in real time. In this study, we have proposed a computational method to detect different types of physical exercises only from photoplethysmography (PPG) signals obtained by a wrist-type wearable device. Our method was composed of feature extraction from PPG and classification using a linear discriminany analysis algorithm. Using the developed method, we could classify two different types of exercises in an individual with accuracy of 78% on average. Our proposed method may be useful to monitor the physical activities of the user and to provide customized u-healthcare services for individuals.
Publisher
Seoul National University Hospital

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