Affective Stress Rating Method Based on Improved Hidden Markov Model_Journal of Biomedical Engineering

Authors：

 LIXin ^1,2 , QIXiaoying ¹ , CHENZetao ¹ , HOUYongjie ³ , YANGYadan ¹ , LIANGQiongyu ¹

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China;
2. The College of Life Science and Bio-Engineering, Beijing University of Technology, Beijing 100124, China;
3. Qinhuangdao Prospect Photoeletric Tech Co., Ltd, Qinhuangdao 066004, China;

Corresponding author：

LIXin, Email: yddylixin@ysu.edu.cn

Keywords：

affective stress rating; affective computing; hidden Markov model; matching process

DOI：

10.7507/1001-5515.20160092

Video：

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Abstract Full text Figures/Tables Video References Cited by

To solve the defect which is recognizing but not rating the stress, or rating but not considering the influence of the previous stress state to the current state of the existing affective stress evaluation method, this paper proposes an approach of affective stress rating model on electrocardiogram (ECG). An affective stress rating algorithm based on hidden Markov model (HMM) was established with the theory of affective computing. The individual's affective stress was rated using this affective rating model combining the investigation questionnaire. Features like complexity and approximate entropy of ECG were used in the model, and a matching process suggested that it improved the accuracy of affective stress rating. The result of the experiment illustrated that the model considering the environmental factors and the influence of previous stress state to the current state was an effective method in affective stress rating, and the accuracy of rating was improved by this affective stress rating method.

Citation： LIXin, QIXiaoying, CHENZetao, HOUYongjie, YANGYadan, LIANGQiongyu. Affective Stress Rating Method Based on Improved Hidden Markov Model. Journal of Biomedical Engineering, 2016, 33(3): 553-558. doi: 10.7507/1001-5515.20160092 Copy

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8.	HEALEY J, PICARD R. Detecting stress during real-world driving tasks using physiological sensors[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(2):156-166.
9.	PICARD R. Affective computing[M]. London:MIT Press, 1997.
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11.	WANG Yujing, MO Jianlin. Emotion feature selection from physiological signals using tabu search[C]//201325th Chinese Control and Decision Conference. Guiyang:2013:3148-3150.
12.	SINGH B, SINGH D, JARYAL A K, et al. Ectopic beats in approximate entropy and sample entropy-based HRV assessment[J]. Int J Syst Sci, 2012, 43(5):884-893.
13.	BOROWSKA M, OCZERETKO E, MAZUREK A, et al. Application of the Lempel-Ziv complexity measure to the analysis of biosignals and medical images[J]. Annual Proceedings of Medical Science, 2005, 50:29-30.
14.	FU B B, FEI X L, SEKAR B D, et al. Research and application of heart sound alignment and descriptor[J]. Computers in biology and medicine, 2013, 43(3):211-218.
15.	黄漫国, 樊尚春,郑德智,等.多传感器数据融合技术研究进展[J].传感器与微系统,2010,29(3):5-8, 12.
16.	RABINER R, JUANG H. An introduction to HMMs[J]. IEEE ASSP Magazine, 1986, 3:4-16.
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18.	PYLKKONEN J, KURIMO M. Analysis of extended Baum-Welch and constrained optimization for discriminative training of HMMs[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(9):2409-2419.

1. PECHTEL P, PIZZAGALLI D A. Effects of early Life stress on cognitive and affective function:an integrated review of human literature[J]. Psychopharmacology (Berl), 2011, 214(1):55-70.
2. COHEN R A. Yerkes-Dodson law[M]//KREUTZER S J, LUCA D J, CAPLAN B. Encyclopedia of clinical neuropsychology. New York:Springer, 2011:2737-2738.
3. BROEK E L, SLUIS F, DIJKSTRA T. Cross-validation of bimodal health-related stress assessment[J]. Personal and Ubiquitous Computing, 2013, 17(2):215-227.
4. COSTIN H, ROTARIU C, PASARICA A. Mental stress detection using heart rate variability and morphologic variability of ECG signals[C]//2012 International Conference and Exposition on Electrical and Power Engineering. Iasi:2012:25-27.
5. MELOLLO P, FORMISANO C, BRACALE U, et al. Classification tree for real-life stress detection using linear Heart Rate Variability analysis. Case study:students under stress due to university examination[C]//World Congress on Medical Physics and Biomedical Engineering. Beijing:2013:477-480.
6. SUBAHNI A R, XIA L, MALIK A S. Association of mental stress with video games[C]//20124th International Conference on Intelligent and Advanced Systems. Kuala Lumpur:2012, 1:82-85.
7. KARTHIKEYAN P, MURUGAPPAN M, YAACOB S. A study on mental arithmetic task based human stress level classification using discrete wavelet transform[C]//2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology. Kuala Lumpur:2012:77-81.
8. HEALEY J, PICARD R. Detecting stress during real-world driving tasks using physiological sensors[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(2):156-166.
9. PICARD R. Affective computing[M]. London:MIT Press, 1997.
10. ASTORINO T, BAKER J, BROCK S, et al. Heart rate variability (HRV) as a tool for diagnostic and monitoring performance in sport and physical activities[J]. Journal of Exercise Physiology online, 2013, 16(3):103-131.
11. WANG Yujing, MO Jianlin. Emotion feature selection from physiological signals using tabu search[C]//201325th Chinese Control and Decision Conference. Guiyang:2013:3148-3150.
12. SINGH B, SINGH D, JARYAL A K, et al. Ectopic beats in approximate entropy and sample entropy-based HRV assessment[J]. Int J Syst Sci, 2012, 43(5):884-893.
13. BOROWSKA M, OCZERETKO E, MAZUREK A, et al. Application of the Lempel-Ziv complexity measure to the analysis of biosignals and medical images[J]. Annual Proceedings of Medical Science, 2005, 50:29-30.
14. FU B B, FEI X L, SEKAR B D, et al. Research and application of heart sound alignment and descriptor[J]. Computers in biology and medicine, 2013, 43(3):211-218.
15. 黄漫国, 樊尚春,郑德智,等.多传感器数据融合技术研究进展[J].传感器与微系统,2010,29(3):5-8, 12.
16. RABINER R, JUANG H. An introduction to HMMs[J]. IEEE ASSP Magazine, 1986, 3:4-16.
17. XIA L, FANG H, ZHANG H. HMM based modeling and health condition assessment for degradation process[C]//201325th Chinese Control and Decision Conference. Guiyang:2013:2945-2948.
18. PYLKKONEN J, KURIMO M. Analysis of extended Baum-Welch and constrained optimization for discriminative training of HMMs[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(9):2409-2419.

Journal of Biomedical Engineering

Affective Stress Rating Method Based on Improved Hidden Markov Model

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