Affective state estimation based on Russell's model and physiological measurements

被引:9
|
作者
Cittadini, Roberto [1 ]
Tamantini, Christian [1 ]
Scotto di Luzio, Francesco [1 ]
Lauretti, Clemente [1 ]
Zollo, Loredana [1 ]
Cordella, Francesca [1 ]
机构
[1] Univ Campus Bio Med Roma, Dept Engn, Res Unit Adv Robot & Human Ctr Technol, Rome, Italy
来源
SCIENTIFIC REPORTS | 2023年 / 13卷 / 01期
关键词
AUTONOMIC NERVOUS-SYSTEM; EMOTION RECOGNITION;
D O I
10.1038/s41598-023-36915-6
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel's model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.
引用
收藏
页数:14
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