Department of Computer Science
SmartMood: Toward pervasive mood tracking and analysis for manic episode detection
This paper describes SmartMood, a mood tracking and analysis system designed for patients with mania. By analyzing the voice data captured from a smartphone while the user is having a conversation, statistics are generated for each behavioral factor to quantitatively describe his/her mood status. By comparing the newly generated statistics with those under normal mood, SmartMood tries to identify any new manic episodes so that appropriate consultation and medication actions can be taken. The daily behavioral statistics may serve as important references for psychiatrists to show the effectiveness of treatments. To reduce the probability of false alarms, we propose an adaptive running range method to estimate the normal mood range for each behavioral factor, and study methods to minimize the effects of background noise on the generated statistics. The preliminary experimental results on SmartMood show that a method using the pitch of a voice data sample to identify silent periods can better differentiate the voice of a normal or manic user in a call session than other methods. The results from the limited proof of concept testing indicate that moving to clinical testing is warranted.
surveillance, Biomedicine, mood disorder, pervasive computing
Source Publication Title
IEEE Transactions on Human-Machine Systems
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Lam, Kam-Yiu, Jiantao Wang, Joseph Kee-Yin Ng, Song Han, Limei Zheng, Calvin Ho Chuen Kam, and Chun Jiang Zhu. "SmartMood: Toward pervasive mood tracking and analysis for manic episode detection." IEEE Transactions on Human-Machine Systems 45.1 (2015): 126-131.