| Summary: | 碩士 === 國立交通大學 === 生醫工程研究所 === 103 === The fast development of internet and information technology brings convenience to modern life, but also brings some side effects. Internet provides people a platform to seek happiness and excitement which may result in over-rely on internet, and leads people to suffer from negative consequences such as education, occupation, and even mental and physiological health. This phenomenon is regarded as "internet addiction". Many researchers had focus on internet addiction and its related issues, and emotion is one of the important topics. Although people use internet to express their feelings, the consequence of internet addiction still can lead to some negative emotion. Despite the fact that some studies had discussed the issue on internet addiction and emotion, the amount of research using physiological reaction is very limited. The physiological signal could reveal more reliable information about the subjects compare with the self-report. Therefore, facial expression as a physiological reaction and will be used in this research in order to investigate the internet addiction subjects. In order to analysis facial expression, we adapted Facial Action Coding System (FACS) as measurement, which is a most widely used method in the research of facial analysis. Next, this research proposed method to recognize FACS. Three databases were used to test the reliability of the proposed method. An experiment was conducted to investigate the emotion reaction on internet addiction subjects. Five films were used as emotion elicitation material and Chen internet addiction scale (CIAS) was used to evaluated the level of internet addiction. The emotion reaction will be investigated by emotional self-report and facial expression, and the facial expression will be analyzed by the proposed methods. The proposed recognition method shows acceptable reliability and discrimination on the testing database in general. The results in the experiment show that internet addiction subjects will show more facial expression than control group and the internet addiction subjects are more likely to inhibit their feelings on the score of emotional self-report. Further, the multiple linear regressions show that facial expression under the anger elicitation can become a potential predictor of internet addiction. To sum up, internet addiction subject shows different emotional reaction than others, which allow people to predict internet addiction by investigate their emotional reaction in the future.
|
|---|
