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 ETSN  Vol.3 No.2 , June 2014
Mood States Recognition of Rowing Athletes Based on Multi-Physiological Signals Using PSO-SVM
Jing Wang, Pei Lei, Kun Wang, Lijuan Mao, Xinyu Chai*
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Abstract: Athletes have various emotions before competition, and mood states have impact on the competi- tion results. Recognition of athletes’ mood states could help athletes to have better adjustment before competition, which is significant to competition achievements. In this paper, physiological signals of female rowing athletes in pre- and post-competition were collected. Based on the multi-physiological signals related to pre- and post-competition, such as heart rate and respiration rate, features were extracted which had been subtracted the emotion baseline. Then the particle swarm optimization (PSO) was adopted to optimize the feature selection from the feature set, and combined with the least squares support vector machine (LS-SVM) classifier. Positive mood states and negative mood states were classified by the LS-SVM with PSO feature optimization. The results showed that the classification accuracy by the LS-SVM algorithm combined with PSO and baseline subtraction was better than the condition without baseline subtraction. The combination can contribute to good classification of mood states of rowing athletes, and would be informative to psychological adjustment of athletes.
Keywords: Affective Computing, Mood States Recognition, Multi-Physiological Signals, PSO, SVM
Cite this paper: Wang, J. , Lei, P. , Wang, K. , Mao, L. and Chai, X. (2014) Mood States Recognition of Rowing Athletes Based on Multi-Physiological Signals Using PSO-SVM. E-Health Telecommunication Systems and Networks, 3, 9-17. doi: 10.4236/etsn.2014.32002.
References

[1]   Jones, G. (1995) More than Just a Game: Research Developments and Issues in Competitive Anxiety in Sport. British Journal of Psychology, 4, 449-478.
http://dx.doi.org/10.1111/j.2044-8295.1995.tb02565.x

[2]   Hanton, S., Thomas, O. and Maynard, I. (2004) Competitive Anxiety Responses in the Week Leading up to Competition: The Role of Intensity, Direction and Frequency Dimensions. Psychology of Sport and Exercise, 2, 169-181.
http://dx.doi.org/10.1016/S1469-0292(02)00042-0

[3]   Lazarus, R.S. (2000) How Emotions Influence Performance in Competitive Sports. The Sport Psychologist, 3, 299-252.

[4]   Woodman, T. and Hardy, L. (2001) Stress and Anxiety. Handbook of Sport Psychology. 290-318.

[5]   Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W. and Taylor, J.G. (2001) Emotion Recognition in Human-Computer Interaction. IEEE Signal Processing Magazine, 1, 32-80.
http://dx.doi.org/10.1109/79.911197

[6]   Kreibig, S.D. (2010) Autonomic Nervous System Activity in Emotion: A Review. Biological Psychology, 3, 394-421.
http://dx.doi.org/10.1016/j.biopsycho.2010.03.010

[7]   Ekman, P., Levenson, R.W. and Friesen, W.V. (1983) Autonomic Nervous System Activity Distinguishes among Emotions. Science, 4616, 1208-1210.
http://dx.doi.org/10.1126/science.6612338

[8]   Picard, R.W. (1997) Affective Computing. The MIT Press, Cambridge, 170.

[9]   Healey, J. and Picard, R. (1998) Digital Processing of Affective Signals. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, Washington.

[10]   Kim, K.H., Bang, S. and Kim, S. (2004) Emotion Recognition System Using Short-Term Monitoring of Physiological Signals. Medical and Biological Engineering and Computing, 3, 419-427.
http://dx.doi.org/10.1007/BF02344719

[11]   Lisetti, C., Nasoz, F., LeRouge, C., Ozyer, O. and Alvarez, K. (2003) Developing Multimodal Intelligent Affective Interfaces for Tele-Home Health Care. International Journal of Human-Computer Studies, 1, 245-255.
http://dx.doi.org/10.1016/S1071-5819(03)00051-X

[12]   Nasoz, F., Lisetti, C.L. and Vasilakos, A.V. (2010) Affectively Intelligent and Adaptive Car Interfaces. Information Sciences, 20, 3817-3836.
http://dx.doi.org/10.1016/j.ins.2010.06.034

[13]   Zheng, J. (2006) Review on the Multidimensional Studies of Competitive Anxiety. Journal of Shenyang Physical EducationInstitute, 3, 58-60 (in Chinese).

[14]   Lv, Y. and Ma, Q. (1995) Research on the Characteristics of Information Processing of Athletes in the Brain with the Events Relative Potentials (ERP). Journal of Tianjin Institute of Physical Education, 4, 1-2 (in Chinese).

[15]   Zhang, Z., Zhou, W., Cai, Z. and Cui, S. (2002) The Characteristics of Brain Function in Chinese Elite Table Tennis Players. Chinese Journal of Sports Medicine, 5, 452-457 (in Chinese).

[16]   Eberhart, R. and Kennedy, J. (1995) A New Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan.
http://dx.doi.org/10.1016/j.ins.2010.06.034

[17]   Suykens, J., Lukas, L., Van Dooren, P., De Moor, B. and Vandewalle, J. (1999) Least Squares Support Vector Machine Classifiers: A Large Scale Algorithm. European Conference on Circuit Theory and Design, ECCTD.

[18]   Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia.
http://dx.doi.org/10.1109/ICNN.1995.488968

[19]   Eberhart, R., Simpson, P. and Dobbins, R. (1996) Computational Intelligence PC Tools. Academic Press Professional, Inc.

[20]   Kennedy, J. (1997) The Particle Swarm: Social Adaptation of Knowledge. IEEE International Conference on Evolutionary Computation, Indianapolis, Indiana.

[21]   Kennedy, J. and Eberhart, R.C. (1997) A Discrete Binary Version of the Particle Swarm Algorithm. Systems, Man, and Cybernetics, 1997 Computational Cybernetics and Simulation, 1997 IEEE International Conference, 5, 4104-4108.

[22]   Vapnik, V. (1963) Pattern Recognition Using Generalized Portrait Method. Automation and Remote Control, 774-780.

[23]   Suykens, J.A. and Vandewalle, J. (1999) Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 3, 293-300.
http://dx.doi.org/10.1023/A:1018628609742

[24]   Osuna, E., Freund, R. and Girosit, F. (1997) Training Support Vector Machines: An Application to Face Detection. Computer Vision and Pattern Recognition, 1997 Proceedings, 1997 IEEE Computer Society Conference, Los Alamitos, 17-19 June 1997, 130-136.

[25]   Fu, M., Yu, Z. and Zhou, X. (2004) Research on Athlete Mental State and Influence Factors Pre-Competition. Sport Science, 2, 48-50 (in Chinese).

[26]   Liu, L. and Shi, Y. (2009) The Definition, State and Developing Trend of Clinical Sport Psychology. China Sport Science and Technology, 6, 76-82 (in Chinese).

[27]   McNair, D.M., Lorr, M. and Droppleman, L.F. (1971) Profile of Mood States. Educational and Industrial Testing Service.

[28]   Grove, J.R. and Prapavessis, H. (1992) Preliminary Evidence for the Reliability and Validity of an Abbreviated Profile of Mood States. International Journal of Sport Psychology, 2, 93-109.

[29]   Zhu, B. (1995) Brief Introduction of POMS Scale and Its Model for China. Journal of Tianjin Institute of Physical Education, 1, 35-37 (in Chinese).

[30]   Lorr, M., McNair, D. and Droppleman, L. (1971) Manual: Profile of Mood States. Educational and Industrial Testing Service, San Diego.

[31]   Morgan, W. (1979) Prediction of Performance in Athletics. Coach, Athletes and the Sport Psychologist, 173-186.

[32]   Cai, S. (2010) Validity and Reliability of the POMS Applied in the Graduates in Sport Major. Journal of West Anhui University, 005, 145-148 (in Chinese).

 
 
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