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

Kam Yiu Lam, Jiantao Wang, Joseph K Y NG, Song Han, Limei Zheng, Calvin Ho Chuen Kam, Chun Jiang Zhu

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Article number6932469
Pages (from-to)126-131
Number of pages6
JournalIEEE Transactions on Human-Machine Systems
Issue number1
Publication statusPublished - 1 Feb 2015

Scopus Subject Areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Biomedicine
  • Mood disorder
  • Pervasive computing
  • Surveillance


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