Jing Wang, Pei Lei, Kun Wang, Lijuan Mao, Xinyu Chai^*

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School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
School of Physical Education, Shanghai Jiao Tong University, Shanghai, China.
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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.

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