[1]WEI Jiaqi,LIU Huaping,WANG Bowen,et al.Extreme learning machine for emotion recognition of tactile gestures[J].CAAI Transactions on Intelligent Systems,2019,14(1):127-133.[doi:10.11992/tis.201804029]
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Extreme learning machine for emotion recognition of tactile gestures

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 1 Page number: 127-133 Column: 学术论文—机器感知与模式识别 Public date: 2019-01-05
Title:
Extreme learning machine for emotion recognition of tactile gestures
Author(s):
WEI Jiaqi1;  LIU Huaping2;  WANG Bowen1;  SUN Fuchun2
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;
2. State Key Lab. of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China
Keywords:
haptic; emotion recognition; extreme learning machine; feature extraction; touch gesture; support vector machine; human-computer interaction; machine learning
CLC:
TP391.4
DOI:
10.11992/tis.201804029
Abstract:
To overcome the deficiencies of sound and image emotion recognition, a new emotion recognition method, haptic emotion recognition, is proposed. A series of haptic emotion recognition studies on Corpus of Social Touch (CoST) datasets were performed. First, the CoST data was preprocessed, presenting some features about haptic emotion recognition. Using the extreme learning machine classifier to explore emotion recognition under different gestures, three kinds of emotions, gentle, normal, and irritable, under 14 kinds of gestures, were identified with higher accuracy and a faster recognition speed (0.04 s). The results showed that differences in gestures will affect the accuracy of emotion recognition, wherein the recognition effect of the gesture "stroke" is the highest in classification accuracy under different classifiers. This new method yielded better classification accuracy, reaching 72.07%. As a classifier of haptic emotion recognition, the extreme learning machine had better classification effect and faster recognition speed. Some gestures corresponded to certain emotions, which affected the classification results.
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