Classification of Physiological Data for Emotion Recognition

被引：13

作者：

Gouverneur, Philip ^{[1
]}

Jaworek-Korjakowska, Joanna ^{[2
]}

Koeping, Lukas ^{[1
]}

Shirahama, Kimiaki ^{[1
]}

Kleczek, Pawel ^{[2
]}

Grzegorzek, Marcin ^{[1
,3
]}

机构：

[1] Univ Siegen, Res Grp Pattern Recognit, Siegen, Germany

[2] AGH Univ Sci & Technol, Dept Automat & Biomed Engn, Krakow, Poland

[3] Univ Econ Katowice, Fac Informat & Commun, Katowice, Poland

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT I | 2017年 / 10245卷

关键词：

D O I：

10.1007/978-3-319-59063-9_55

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Emotion recognition is seen to be important not only for computer science or sport activity but also for old and sick people to live independently in their own homes as long as possible. In this paper Empatica E4 wristband is used to collect the date and assess the stress level of the user. We describe an algorithm for the classification of physiological data for emotion recognition. The algorithm has been divided into the following steps: data acquisition, signal preprocessing, feature extraction, and classification. The data acquired during various daily activities consist of more than 3 h of wristband signal. Through various stress tests we achieve a maximum accuracy of 71% for a stressed/relaxed classification. These results lead to the conclusion that Empatica E4 wristband can be used as a device for emotion recognition.

引用

页码：619 / 627

页数：9

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