An Early Negative Emotion Detection System Based on Smartphone Usage Patterns

Abstract

According to the World Health Organization (WHO) depression is currently one of many serious problems and awareness of negative emotions is helpful for treating it Behavioral patterns can either be an antecedent or a consequence of human emotion For example usage patterns on smartphones can reflect the user’s emotion With the popularity of smartphone ownership researchers are beginning to examine the association of smartphone usage patterns with emotional conditions This study uses smart phone usage patterns to detect emotional states aiming to improve self-awareness of negative emotion We developed three Visual Analogue Scales to measure and mark the emotional status The package names of applications shown on the light-on screen are recorded as phone usages The timeslots were set for each emotion mark in order to determine whether a usage feature is associated with the mark or not Different users may have different usage patterns that reflect their emotions We utilize several feature selection methods and classifiers to determine personalized usage features for the machine learning In summary we considered four timeslots five feature selection methods and four classifiers; each combination can be viewed as a model Finally we developed a detection model selection method based on Rank product scoring to narrow down the combinations and to choose the best combination for the detection model The user has his/her distinct behavioral pattern on the smartphone This unique data was used to train our personalized detection model All personalized detection models achieved an average accuracy of 81 98 % 84 58 % and 82 96 % for detecting depression anxiety and stress respectively and outperformed the two baseline methods: linear regression (as applied by Microsoft’s MoodScope system) and general guessing The general guessing method considers all detections according to the level of emotional conditions that appears most frequently The personalized models were sent back to subjects for further evaluation and the results showed that the models predicted their emotional states with an accuracy rate of 85 9% We have developed an early negative emotion detection model for smartphones that after a 14-day personalized training period is able to detect negative emotional states based on the smartphone usage patterns two hours before detection This model has a potential for ecological momentary intervention for depressive disorders by envisioning negative emotions and informing the users how they have interacted with the smartphone before they actually reach negative emotional status

Date of Award	2014 Jul 28
Original language	English
Supervisor	Jung-Hsien Chiang (Supervisor)