Developing a Smart IoT Solution to Monitor on-Bed Movement Patterns
Conference proceedings article
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Strategic Research Themes
Publication Details
Author list: Youngkong P., Panpanyatep W., Thamrongaphichartkul K.
Publisher: Hindawi
Publication year: 2020
Start page: 306
End page: 309
Number of pages: 4
ISBN: 9781728166940
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
IoT devices and systems become a part of modern living. They are mostly used to monitor daily activities, especially related to personal health and fitness. In fact, it is getting more crucial during the COVID-19 pandemic. In this study, a smart monitoring and alarming IoT system called 'NEF' was modified to recognize on-bed movement patterns including prone position applying different machine learning techniques. On-bed movement patterns were collected from 7 subjects. Considering only prone and supine positions, the models obtained from multilayer perceptron was the best. However, random forest yielded the highest overall correctly classified percentage. Further investigation is likely to include beddings such as pillows and blankets. © 2020 IEEE.
Keywords
Monitor and Alarm, On-Bed Movement, Prone Position
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