TITLE:
Mood States Recognition of Rowing Athletes Based on Multi-Physiological Signals Using PSO-SVM
AUTHORS:
Jing Wang, Pei Lei, Kun Wang, Lijuan Mao, Xinyu Chai
KEYWORDS:
Affective Computing, Mood States Recognition, Multi-Physiological Signals, PSO, SVM
JOURNAL NAME:
E-Health Telecommunication Systems and Networks,
Vol.3 No.2,
May
15,
2014
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.