Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

SmartMood: Toward pervasive mood tracking and analysis for manic episode detection

Language

English

Abstract

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.

Keywords

surveillance, Biomedicine, mood disorder, pervasive computing

Publication Date

2015

Source Publication Title

IEEE Transactions on Human-Machine Systems

Volume

45

Issue

1

Start Page

126

End Page

131

Publisher

Institute of Electrical and Electronics Engineers

Peer Reviewed

1

DOI

10.1109/THMS.2014.2360469

Link to Publisher's Edition

http://dx.doi.org/10.1109/THMS.2014.2360469

ISSN (electronic)

21682291

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