Research on the Performance Comparing and Building of Affective Computing Database Based on Physiological Parameters_Journal of Biomedical Engineering

Authors：

 LIXin ¹ , DUXiaojuan ¹ , ZHANGYunpeng ¹ , YINGLijuan ¹ , LIChangwu ²

1. Institute of Biomedical Engineering, Yanshan University;Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao 066004, China;
2. Dalian Polytechnic University, Dalian 116000, China;

Corresponding author：

LIXin, Email: yddylixin@ysu.edu.cn

Keywords：

affective computing; psychological stress; electrocardiogram; electromyogram; electroencephalogram

DOI：

10.7507/1001-5515.20140146

Video：

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Abstract Full text Figures/Tables Video References Cited by

The validity and reasonableness of emotional data are the key issues in the cognitive affective computing research. Effects of the emotion recognition are decided by the quality of selected data directly. Therefore, it is an important part of affective computing research to build affective computing database with good performance, so that it is the hot spot of research in this field. In this paper, the performance of two classical cognitive affective computing databases, the Massachusetts Institute of Technology (MIT) cognitive affective computing database and Germany Augsburg University emotion recognition database were compared, their data structure and data types were compared respectively, and emotional recognition effect based on the data were studied comparatively. The results indicated that the analysis based on the physical parameters could get the effective emotional recognition, and would be a feasible method of pressure emotional evaluation. Because of the lack of stress emotional evaluation data based on the physiological parameters domestically, there is not a public stress emotional database. We hereby built a dataset for the stress evaluation towards the high stress group in colleges, candidates of postgraduates of Ph.D and master as the subjects. We then acquired their physiological parameters, and performed the pressure analysis based on this database. The results indicated that this dataset had a certain reference value for the stress evaluation, and we hope this research can provide a reference and support for emotion evaluation and analysis.

Citation： LIXin, DUXiaojuan, ZHANGYunpeng, YINGLijuan, LIChangwu. Research on the Performance Comparing and Building of Affective Computing Database Based on Physiological Parameters. Journal of Biomedical Engineering, 2014, 31(4): 782-787. doi: 10.7507/1001-5515.20140146 Copy

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1. PICARD R W. Affective computing[M]. London: MIT Press, 1997.
2. KIM J, ANDRÉ E. Multi-channel biosignal analysis for automatic emotion recognition[C]//Proceedings of the First International Conference on Biomedical Electronics and Devices. Funchal, Madeira, Portugal: 2008: 124-131.
3. PICARD R W, HEALEY J. Affective wearable[C]//Proceedings of the First International Symposium on Wearable Computers. Cambridge, USA: 1997: 90.
4. JIANG H P,YANG G S,GUI X K,et al. Emotion recognition system design using multi-physiological signals[C]//2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing. Kyoto: 2012: 499-503.
5. VILLON O, LISETTI C. Toward recognizing individual's subjective emotion from physiological signals in pactical application[C]//20th IEEE International Symposium on Computer-Based Medical Systems. Mafibor: 2007: 357-362.
6. KIM J, ANDRÉ E. Emotion recognition based on physiological changes in music listening[J]. IEEE Trans Pattern Anal Mach Intell, 2008, 30(12): 2067-2083.
7. KIM K H, BANG S W, KIM S R. Emotion recognition system using short-term monitoring of physiological signals[J]. Med Biol Eng Comput, 2004, 42(3): 419-427.
8. GUO X H. Research on emotion recognition based on physiological signal and AuBT[C]//2011 International Conference on Consumer Electronics, Communications and Networks. Xianning, 2011: 614-617.
9. HEALEY J A. Wearable and automotive systems for affect recognition from physiology[D]. Boston: Massachusetts Institute of Technology, 2000.
10. WAGNER J, KIM J, ANDRÉ E. From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification[C]//IEEE International Conference on Multimedia and Expo. Amsterdam: 2005: 940-943.
11. 李昕,马利,王金甲,等.特征选择(FS)算法在生物信息学中的应用[J].生物医学工程学杂志,2011,28(2):410-414.
12. JAHNCKE H, HYGGE S, HALIN N, et al. Open-plan office noise: cognitive performance and restoration[J]. J Environ Psych, 2011, 31(4): 373-382.
13. 程颖.肌电信号在情感状态识别中的研究[D].重庆:西南大学,2011.
14. 李昕,王惠惠,王月茹,等.基于能量估计的小波阈值与经验模态分解相结合的滤波方法研究[J].生物医学工程学杂志,2011,28(6):1098-1102.
15. LI X, WANG Y R, SONG J L, et al. Research on classification method of combining support vector machine and genetic algorithm for motor imagery EEG[J]. J Comput Inform Syst, 2011, 7(12): 4351-4358.

Journal of Biomedical Engineering

Research on the Performance Comparing and Building of Affective Computing Database Based on Physiological Parameters

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