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http://dx.doi.org/10.5573/ieie.2015.52.6.126

Sleep/Wake Dynamic Classifier based on Wearable Accelerometer Device Measurement  

Park, Jaihyun (Dept. of Electronic Engineering, Korea Univ.)
Kim, Daehun (Dept. of Electronic Engineering, Korea Univ.)
Ku, Bonhwa (Dept. of Visual Information Processing, Korea Univ.)
Ko, Hanseok (Dept. of Electronic Engineering, Korea Univ.)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.6, 2015 , pp. 126-134 More about this Journal
Abstract
A sleep disorder is being recognized as one of the major health issues related to high levels of stress. At the same time, interests about quality of sleep are rapidly increasing. However, diagnosing sleep disorder is not a simple task because patients should undergo polysomnography test, which requires a long time and high cost. To solve this problem, an accelerometer embedded wrist-worn device is being considered as a simple and low cost solution. However, conventional methods determine a state of user to "sleep" or "wake" according to whether values of individual section's accelerometer data exceed a certain threshold or not. As a result, a high miss-classification rate is observed due to user's intermittent movements while sleeping and tiny movements while awake. In this paper, we propose a novel method that resolves the above problems by employing a dynamic classifier which evaluates a similarity between the neighboring data scores obtained from SVM classifier. A performance of the proposed method is evaluated using 50 data sets and its superiority is verified by achieving 88.9% accuracy, 88.9% sensitivity, and 88.5% specificity.
Keywords
가속도 센서;수면/비수면;동적 분류기;웨어러블 디바이스;헬스 케어;
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1 A. M. Bianchi and O. P. Villantieri et al, "Improving actigraph sleep/wake classification with cardio-respiratory signals", IEEE International Conference on Engineering in Medicine and Biology Society, pp. 3517-3520, New York, USA, August-September 2006.
2 A. Sadeh and C. Acebo, "The role of actigraphy in sleep medicine", Sleep medicine reviews, Vol. 6, no. 2, pp. 113-124, May 2002.   DOI   ScienceOn
3 R. J. Cole and D. F. Kripke et al, "Automatic Sleep/Wake Identification From Wrist Activity", Sleep, Vol. 15, no. 5, pp. 461-469, October 1992.   DOI
4 A. Sadeh and K. M. Sharkey et al, "Activity-based sleep-wake identification: an empirical test of methodological issues", Sleep, Vol. 17, no. 3, pp. 201-207, April 1994.   DOI
5 G. Jean-Louis and D. F. Kripke et al, "Sleep estimation from wrist movement quantified by different actigraphic modalities", Journal of Neuroscience Methods, Vol. 105, no. 2, pp. 185-191, February 2001.   DOI   ScienceOn
6 W. Karlen and C. Mattiussi et al, "Improving actigraph sleep/wake classification with cardio-respiratory signals", IEEE International Conference on Engineering in Medicine and Biology Society, pp. 5262-5265, Vancouver, Canada, August 2008.
7 A. H. Hokkanen and L. Hanninen et al, "Predicting sleep and lying time of calves with a support vector machine classifier using accelerometer data", Applied Animal Behaviour Science, Vol. 134, no. 1-2, pp. 10-15, October 2011.   DOI   ScienceOn
8 A. Y. Ng and M. I. Jordan et al, "On Spectral Clustering: Analysis and an algorithm", Advances in Neural Information Processing Systems, Vol. 14, pp. 849-856, 2001.
9 S. Amari and S. Wu, "Improving support vector machine classifiers by modifying kernel functions", Neural Networks, Vol. 12, no. 6, pp. 783-789, July 1999.   DOI   ScienceOn
10 W. Karlen and D. Floreano, "Adaptive Sleep-Wake Discrimination for Wearable Devices", IEEE Transactions on Biomedical Engineering, Vol. 58, no. 4, pp. 920-926, April 2011.   DOI   ScienceOn