Preprint Article Version 1 This version is not peer-reviewed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)

How to cite: Jeng, P.; Wang, L.; Hu, C.; Wu, D. A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach. Preprints 2019, 2019070060 (doi: 10.20944/preprints201907.0060.v1). Jeng, P.; Wang, L.; Hu, C.; Wu, D. A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach. Preprints 2019, 2019070060 (doi: 10.20944/preprints201907.0060.v1).

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.

Subject Areas

IoT; wearable device; machine learning; streaming data; sleep posture

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