Inferring mood instability via smartphone sensing: A multi-view learning approach

Xiao Zhang, Fuzhen Zhuang, Wenzhong Li, Haochao Ying, Hui Xiong, Sanglu Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

A high correlation between mood instability (MI), the rapid and constant fluctuation in mood, and mental health has been demonstrated. However, conventional approaches to measure MI are limited owing to the high manpower and time cost required. In this paper, we propose a smartphone-based MI detection that can automatically and passively detect MI with minimal human involvement. The proposed method trains a multi-view learning classification model using features extracted from the smartphone sensing data of volunteers and their self-reported moods. The trained classifier is then used to detect the MI of unseen users efficiently, thereby reducing the human involvement and time cost significantly. Based on extensive experiments conducted with the dataset collected from 68 volunteers, we demonstrate that the proposed multi-view learning model outperforms the baseline classifiers.

Original languageEnglish (US)
Title of host publicationMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages1401-1409
Number of pages9
ISBN (Electronic)9781450368896
DOIs
StatePublished - Oct 15 2019
Externally publishedYes
Event27th ACM International Conference on Multimedia, MM 2019 - Nice, France
Duration: Oct 21 2019Oct 25 2019

Publication series

NameMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia

Conference

Conference27th ACM International Conference on Multimedia, MM 2019
Country/TerritoryFrance
CityNice
Period10/21/1910/25/19

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Media Technology

Keywords

  • Attention
  • Mood Instability Detection
  • Multi-view Learning
  • Smartphone Sensing

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