SVM based dynamic classifier for sleep disorder monitoring wearable device

Jaihyun Park; Daehun Kim; Cheoljong Yang; Hanseok Ko

doi:10.1109/ICCE.2016.7430624

SVM based dynamic classifier for sleep disorder monitoring wearable device

Jaihyun Park, Daehun Kim, Cheoljong Yang, Hanseok Ko

School of Electrical Engineering

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

4 Citations (Scopus)

Abstract

An accelerometer embedded wrist-worn device is widely used for sleep assessment. However, conventional methods determine a state of user to «sleep» or «wakefulness» according to whether the accelerometer value of individual epoch exceeds a certain threshold or not. As a result, high miss-classification rate is observed due to user's small intermittent movements while sleeping and short term movements while awake. In this paper, a novel approach is proposed that mitigates such problems by employing a dynamic classifier which analyzes similarity between the neighboring data scores obtained from support vector machine classifier. Performance of the proposed method is evaluated using 50 real data sets and its superiority is verified.

Original language	English
Title of host publication	2016 IEEE International Conference on Consumer Electronics, ICCE 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	309-310
Number of pages	2
ISBN (Print)	9781467383646
DOIs	https://doi.org/10.1109/ICCE.2016.7430624
Publication status	Published - 2016 Mar 10
Event	IEEE International Conference on Consumer Electronics, ICCE 2016 - Las Vegas, United States Duration: 2016 Jan 7 → 2016 Jan 11

Other

Other	IEEE International Conference on Consumer Electronics, ICCE 2016
Country/Territory	United States
City	Las Vegas
Period	16/1/7 → 16/1/11

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering

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10.1109/ICCE.2016.7430624

Cite this

SVM based dynamic classifier for sleep disorder monitoring wearable device. / Park, Jaihyun; Kim, Daehun; Yang, Cheoljong et al.
2016 IEEE International Conference on Consumer Electronics, ICCE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 309-310 7430624.

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

Park, J, Kim, D, Yang, C & Ko, H 2016, SVM based dynamic classifier for sleep disorder monitoring wearable device. in 2016 IEEE International Conference on Consumer Electronics, ICCE 2016., 7430624, Institute of Electrical and Electronics Engineers Inc., pp. 309-310, IEEE International Conference on Consumer Electronics, ICCE 2016, Las Vegas, United States, 16/1/7. https://doi.org/10.1109/ICCE.2016.7430624

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abstract = "An accelerometer embedded wrist-worn device is widely used for sleep assessment. However, conventional methods determine a state of user to «sleep» or «wakefulness» according to whether the accelerometer value of individual epoch exceeds a certain threshold or not. As a result, high miss-classification rate is observed due to user's small intermittent movements while sleeping and short term movements while awake. In this paper, a novel approach is proposed that mitigates such problems by employing a dynamic classifier which analyzes similarity between the neighboring data scores obtained from support vector machine classifier. Performance of the proposed method is evaluated using 50 real data sets and its superiority is verified.",

author = "Jaihyun Park and Daehun Kim and Cheoljong Yang and Hanseok Ko",

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booktitle = "2016 IEEE International Conference on Consumer Electronics, ICCE 2016",

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N2 - An accelerometer embedded wrist-worn device is widely used for sleep assessment. However, conventional methods determine a state of user to «sleep» or «wakefulness» according to whether the accelerometer value of individual epoch exceeds a certain threshold or not. As a result, high miss-classification rate is observed due to user's small intermittent movements while sleeping and short term movements while awake. In this paper, a novel approach is proposed that mitigates such problems by employing a dynamic classifier which analyzes similarity between the neighboring data scores obtained from support vector machine classifier. Performance of the proposed method is evaluated using 50 real data sets and its superiority is verified.

AB - An accelerometer embedded wrist-worn device is widely used for sleep assessment. However, conventional methods determine a state of user to «sleep» or «wakefulness» according to whether the accelerometer value of individual epoch exceeds a certain threshold or not. As a result, high miss-classification rate is observed due to user's small intermittent movements while sleeping and short term movements while awake. In this paper, a novel approach is proposed that mitigates such problems by employing a dynamic classifier which analyzes similarity between the neighboring data scores obtained from support vector machine classifier. Performance of the proposed method is evaluated using 50 real data sets and its superiority is verified.

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SVM based dynamic classifier for sleep disorder monitoring wearable device

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