Bayesian Network Based Emotion Prediction and Feedback System for Smartphones

  • 陳 彥霖

Student thesis: Master's Thesis


Most people do not quite understand about the depression and they think that is just in a bad mood and do not need any treatment In the long-term accumulation of pressure the people are harder and harder to perceive their own negative emotion If there is an appropriate mechanism to remind the user to do some activities relieve pressure and calm emotions it's helpful for avoiding long melancholy mood accumulation This study cooperates with professional psychiatrists to develop a detection system on APPs and using the package name of APP which displayed on screen as the basic information about smartphone using behaviors When the system recognizes the negative mood it would give appropriate feedback and help the user to ease their negative emotions and realize the reason that may cause the negative emotions We use Bayesian network structure which we defined for each user to create personal model and then use it to predict the user's mood and calculate the most likely factor as a feedback After a 14-day personalized training period it is able to detect emotional states based on the smartphone usage patterns at last 30 minutes The average accuracy of the experiment is 54% and we can also find that the patients' average accuracy and average using time on smartphone both are higher than normal users in any time slot Comparing with the case of the classifiers the best accuracy is only less than Na?ve Bayes 1% and the advantage of Bayesian network is not a black-box classification
Date of Award2016 Aug 11
Original languageEnglish
SupervisorJung-Hsien Chiang (Supervisor)

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