Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objectives: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. Methods: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and we evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). Results: We designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. The experimental results showed that the proposed model achieved an accuracy and a specificity of 66.23% and 75.32%, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. Conclusions: The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.
Abstract
Objectives: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. Methods: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and we evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). Results: We designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. The experimental results showed that the proposed model achieved an accuracy and a specificity of 66.23% and 75.32%, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. Conclusions: The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.
참고문헌 (27)
- "Articles on stress and anxiety disorder that destroy me" (2017)[URL]
- 윤지현, "건강한 성인의 주관적 건강상태와 스트레스 인지, 건강 관련 습관, 혈청 생화학 지표 및 영양 섭취와의 관계", 한국식품영양학회지, 30(1), pp.83~95 (2017), 한국식품영양학회
- 전현규, "스트레스와 신체활동, 그리고 건강관련 삶의 질의 연관성에 대한 실증분석: 2008~2013년 KNHANES 데이터분석", 한국산학기술학회논문지, 16(8), pp.5351~5363 (2015), 한국산학기술학회[DOI]
- 황종문, "신체활동, 흡연, 음주에 따른 한국성인의 건강행동 집단분류와 정신건강과의 관계", 한국웰니스학회, 11(4), pp.369~379 (2016), 한국웰니스학회[DOI]
- 임채영, "무선 심전도측정을 통한 스트레스 평가에 관한 연구", 한국컴퓨터정보학회논문지, 16(2), pp.17~24 (2011), 한국컴퓨터정보학회
- 조영창, "스트레스 평가를 위한 심박 변이도, 전기피부반응 및 피부온도 특성", 한국산업정보학회논문지, 20(3), pp.11~18 (2015), 한국산업정보학회
- Xu Q, "Cluster-based analysis for personalized stress evaluation using physiological signals", IEEE J Biomed Health Inform, 19(1), pp.275~281 (2015)
- Sani MM, "Support vector machine for classification of stress subjects using EEG signals", Proceedings of 2014 IEEE Conference on Systems, Process and Control (ICSPC), pp.127~131 (2014)
- Litjens G, "A survey on deep learning in medical image analysis", Med Image Anal, 42, pp.60~88 (2017)
- Hinton GE, "A fast learning algorithm for deep belief nets", Neural Comput, 18(7), pp.1527~1554 (2006)
- Hinton GE, "Reducing the dimensionality of data with neural networks", Science, 313(5786), pp.504~507 (2006)
- Lee H, "Sparse deep belief net model for visual area V2", Adv Neural Inf Process Syst, 20, pp.873~880 (2008)
- Krizhevsky A, "Learning multiple layers of features from tiny images", master, Department of Computer Science, pp.2009
- Vincent P, "Extracting and composing robust features with denoising autoencoders", Proceedings of the 25th International Conference on Machine Learning, pp.1096~1103 (2008)
- Dahl GE, "Context-dependent pre-trained deep neural networks for large vocabulary speech recognition", IEEE Trans Audio Speech Lang Process, 20(1), pp.30~42 (2012)
- Mishra C, "Deep machine learning and neural networks : an overview", Int J Hybrid Inf Technol, 9(11), pp.401~414 (2016)
- Abdel-Zaher AM, "Breast cancer classification using deep belief networks", Expert Syst Appl, 46, pp.139~144 (2016)
- Tamilselvan P, "Failure diagnosis using deep belief learning based health state classification", Reliab Eng Syst Saf, 115, pp.124~135 (2013)
- Fakoor R, "Using deep learning to enhance cancer diagnosis and classification", Proceedings of the International Conference on Machine Learning (ICML) (2013)
- "The six Korea National Health & Nutrition Examination Survey 2013-2015 (KNHANES VI)" (2017)[URL]
- "Confusion matrix" (2016)[URL]
- "DL4J. Tutorial: deep-belief networks & MNIST" (2017)[URL]
- "DL4J. A beginner’s tutorial for restricted Boltzmann machines" (2017)[URL]
- "History of deep running: site that summarizes the history of deep running" (2014)[URL]
- "DL4J. Deep learning for Java" (2017)[URL]
- "Dl4J-examples (Java)" (2017)[URL]
- Saito G, "Deep learning from scratch", pp.103~119 (2017), Hanbit Media