SVM based dynamic classifier for sleep disorder monitoring wearable device

Jaihyun Park, Daehun Kim, Cheoljong Yang, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 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 languageEnglish
Title of host publication2016 IEEE International Conference on Consumer Electronics, ICCE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-310
Number of pages2
ISBN (Print)9781467383646
DOIs
Publication statusPublished - 2016 Mar 10
EventIEEE International Conference on Consumer Electronics, ICCE 2016 - Las Vegas, United States
Duration: 2016 Jan 72016 Jan 11

Other

OtherIEEE International Conference on Consumer Electronics, ICCE 2016
CountryUnited States
CityLas Vegas
Period16/1/716/1/11

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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  • Cite this

    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 (pp. 309-310). [7430624] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2016.7430624