Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

• Yusuke, Fukazawa

• Naoki, Yamamoto

• Takashi, Hamatani

• Keiichi, Ochiai

• Akira, Uchiyama

• Ken, Ohta

抄録

Monitoring mental health has received considerable attention as a countermeasure against the increasing occurrence of mental illness worldwide. However, current monitoring services incur costs because users are required to attach wearable devices or answer questions. To reduce such costs, many studies have used smartphone-based passive sensing technology to capture a user's mental state. This paper reviews those studies from the perspective of machine learning and statistical analysis. Forty-four studies published since 2011 have been reviewed and summarized from three perspectives: designed features, machine learning algorithm, and evaluation method. The features considered include location and mobility, activity, speech, sleep, phone usage, and context features. Tasks are classified as correlation analysis, regression tasks, and classification tasks. The machine learning algorithm used for each task is summarized. Evaluation metrics and cross validation methods are also summarized. For those who are not necessarily machine learning experts, we aim to provide information on typical machine learning framework for smartphone-based mental state estimation. For experts in the field, we hope this review will be a helpful tool to check for potential omissions.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.28(2020) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.28.16------------------------------

Monitoring mental health has received considerable attention as a countermeasure against the increasing occurrence of mental illness worldwide. However, current monitoring services incur costs because users are required to attach wearable devices or answer questions. To reduce such costs, many studies have used smartphone-based passive sensing technology to capture a user's mental state. This paper reviews those studies from the perspective of machine learning and statistical analysis. Forty-four studies published since 2011 have been reviewed and summarized from three perspectives: designed features, machine learning algorithm, and evaluation method. The features considered include location and mobility, activity, speech, sleep, phone usage, and context features. Tasks are classified as correlation analysis, regression tasks, and classification tasks. The machine learning algorithm used for each task is summarized. Evaluation metrics and cross validation methods are also summarized. For those who are not necessarily machine learning experts, we aim to provide information on typical machine learning framework for smartphone-based mental state estimation. For experts in the field, we hope this review will be a helpful tool to check for potential omissions.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.28(2020) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.28.16------------------------------

収録刊行物

• 情報処理学会論文誌

  情報処理学会論文誌 61 (1), 2020-01-15

キーワード

• [特集：活き活きとしたスマートシティを実現する高度交通システムとパーベイシブシステム（招待論文）] smartphone, sensing, mental health, machine learning

詳細情報

• CRID: 1050845764250631936
• NII論文ID: 170000181591
• NII書誌ID: AN00116647
• ISSN: 18827764
• Web Site: http://id.nii.ac.jp/1001/00202622/
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles