Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress

Chiapin Wang; Tsung Yi Fan Chiang; Shih Hau Fang; Chieh Ju Li; Yeh Liang Hsu

doi:10.1109/AICAS.2019.8771632

Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress

Chiapin Wang, Tsung Yi Fan Chiang, Shih Hau Fang, Chieh Ju Li, Yeh Liang Hsu

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

This paper presents a novel sleep-status discrimination system by adopting a motion sensing mattress which detects the user's activities on bed including the movement of head, chest, legs and feet. Unlike traditional methods like Polysomnography (PSG) which needs electrical equipment connected to users, or like wrist actigraphy which needs to be contact to users, the proposed system distinguishes sleep states in a non-conscious and non-contact way. The proposed system is built by a machine learning technique in the offline stage, and distinguishes sleep states in the online stage by using our designed sleep-status discrimination algorithm. The experimental results illustrate that the proposed method efficiently distinguishes sleep statuses without using a wearable device contact to body or using PSG diagnosis undertaken at hospitals.

Original language	English
Title of host publication	Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	160-162
Number of pages	3
ISBN (Electronic)	9781538678848
DOIs	https://doi.org/10.1109/AICAS.2019.8771632
Publication status	Published - 2019 Mar
Event	1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan Duration: 2019 Mar 18 → 2019 Mar 20

Publication series

Name	Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference	1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Country/Territory	Taiwan
City	Hsinchu
Period	2019/03/18 → 2019/03/20

Keywords

Sleep-status discrimination
machine learning

ASJC Scopus subject areas

Artificial Intelligence
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/AICAS.2019.8771632

Cite this

Wang, C., Chiang, T. Y. F., Fang, S. H., Li, C. J., & Hsu, Y. L. (2019). Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress. In Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 (pp. 160-162). Article 8771632 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICAS.2019.8771632

Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress. / Wang, Chiapin; Chiang, Tsung Yi Fan; Fang, Shih Hau et al.
Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 160-162 8771632 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, C, Chiang, TYF, Fang, SH, Li, CJ & Hsu, YL 2019, Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress. in Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019., 8771632, Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 160-162, 1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Hsinchu, Taiwan, 2019/03/18. https://doi.org/10.1109/AICAS.2019.8771632

Wang C, Chiang TYF, Fang SH, Li CJ, Hsu YL. Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress. In Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 160-162. 8771632. (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). doi: 10.1109/AICAS.2019.8771632

Wang, Chiapin ; Chiang, Tsung Yi Fan ; Fang, Shih Hau et al. / Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress. Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 160-162 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019).

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abstract = "This paper presents a novel sleep-status discrimination system by adopting a motion sensing mattress which detects the user's activities on bed including the movement of head, chest, legs and feet. Unlike traditional methods like Polysomnography (PSG) which needs electrical equipment connected to users, or like wrist actigraphy which needs to be contact to users, the proposed system distinguishes sleep states in a non-conscious and non-contact way. The proposed system is built by a machine learning technique in the offline stage, and distinguishes sleep states in the online stage by using our designed sleep-status discrimination algorithm. The experimental results illustrate that the proposed method efficiently distinguishes sleep statuses without using a wearable device contact to body or using PSG diagnosis undertaken at hospitals.",

keywords = "Sleep-status discrimination, machine learning",

author = "Chiapin Wang and Chiang, {Tsung Yi Fan} and Fang, {Shih Hau} and Li, {Chieh Ju} and Hsu, {Yeh Liang}",

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AB - This paper presents a novel sleep-status discrimination system by adopting a motion sensing mattress which detects the user's activities on bed including the movement of head, chest, legs and feet. Unlike traditional methods like Polysomnography (PSG) which needs electrical equipment connected to users, or like wrist actigraphy which needs to be contact to users, the proposed system distinguishes sleep states in a non-conscious and non-contact way. The proposed system is built by a machine learning technique in the offline stage, and distinguishes sleep states in the online stage by using our designed sleep-status discrimination algorithm. The experimental results illustrate that the proposed method efficiently distinguishes sleep statuses without using a wearable device contact to body or using PSG diagnosis undertaken at hospitals.

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Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress

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