Study of User’s Affective Response on Multimedia Contents Using Physiological Signal
Date Issued
2006
Date
2006
Author(s)
SHEN, CHI-TE
DOI
zh-TW
Abstract
It is important to understand the user’s feeling and feedback by user’s emotional expression in human-computer interaction. The aim of this study is to develop a affective response recognition system by bio-signals measurement, feature extraction and classification. The IAPS (International Affective Picture System) is adopted to elicit user’s affective responses included high valence high arousal, high valence low arousal, low valence high arousal, and low valence low arousal. Moreover, the prepared video clips are also used to elicit multi-user’s and single user’s affective responses included laughing, pleasure, disgust, and fear. The user’s physiological signals, EMG, ECG, blood pulse, and respiration signal, would be measured and recorded simultaneous. By normalization, signal post-processing and feature extraction, biophysical signals would be classified by KNN classifier to indicate the corresponding affective response.
The results show that the accuracy of using IAPS, Video to elicit multi-users’ affective response and using Video to elicit single user’s affective response are 90.87%, 95.32%, and 96.58 %. If data with only top-10 important features which obtained by evaluating the information gain is used to classify, the accuracy would become 91.45%, 96.1%, and 97.61%。
If the data of specific one user is used as the testing dataset and other is used as the training dataset, the accuracy would drop off only 31.93%. The main reason is the number of user is not enough, and the differences between each individual are obviously. Besides, if we separate the data of specific one user by n-fold cross-validation as the training dataset and testing dataset, the accuracy would maintain higher about 95.47%. Therefore, in order to overcome it, more experiments and more biophysical signals are necessary due to the difference between each individual would affect the accuracy of recognition seriously.
At the last of this thesis we have discussed about the tough question of studying user’s affective response recognition system and provided the suggestions and strategies to a real-time user’s affective response recognition system.
Subjects
人機介面
情緒辨識
IAPS
生理訊號
KNN
human-computer interface
emotion recognition
physiological signal
Type
thesis
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