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Preserved in Portico This version is not peer-reviewed
A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach
Version 1
: Received: 1 July 2019 / Approved: 3 July 2019 / Online: 3 July 2019 (09:55:40 CEST)
A peer-reviewed article of this Preprint also exists.
Jeng, P.-Y.; Wang, L.-C.; Hu, C.-J.; Wu, D. A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach. Sensors 2021, 21, 258, doi:10.3390/s21010258. Jeng, P.-Y.; Wang, L.-C.; Hu, C.-J.; Wu, D. A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach. Sensors 2021, 21, 258, doi:10.3390/s21010258.
Abstract
Sleep postures monitoring systems in the hospital aim at transforming sensing signals into quantitative data to characterize the sleep behaviors of the patient. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost - a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve 90 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easily-wear wrist sensor can accurately quantify sleep postures.
Keywords
IoT; wearable device; machine learning; streaming data; sleep posture
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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