SmartMood: Toward Pervasive Mood Tracking and Analysis for Manic Episode Detection
IEEE T. Human-Machine Systems(2015)
摘要
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.
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关键词
patient mood analysis system,biomedical telemetry,biomedicine,medical consultation,psychiatrist-patient call session,medical disorders,smartphone conversation,man-machine systems,statistics,telemedicine,smartphone call session,voice data analysis,patient mood tracking system,pervasive mood analysis,daily behavioral statistics,voice pitch data sample,mood disorder,patient monitoring,pervasive computing,speech-based user interfaces,surveillance,silent period identification,psychology,patient care,normal mood range,pervasive mood tracking,background noise effects,quantitative mood status description,manic smartphone user voice,normal smartphone user voice,false alarm probability reduction,adaptive running range method,smartmood,smart phones,medication actions,behavioral factor,patient diagnosis,patient treatment,manic episode detection,treatment effectiveness,manic episode identification,noise measurement,bismuth,speech
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