Deep Learning for Mobile Mental Health: Challenges and recent advances

doi:10.1109/MSP.2021.3099293

	Deep Learning for Mobile Mental Health: Challenges and recent advances
	Han, Jing 1; Zhang, Zixing 2; Mascolo, Cecilia 1; Andre, Elisabeth 3; Tao, Jianhua4 ; Zhao, Ziping 5,6; Schuller, Bjoern W.6,7
发表期刊	IEEE SIGNAL PROCESSING MAGAZINE
ISSN	1053-5888
	2021-11-01
卷号	38 期号:6 页码:96-105
通讯作者	Han, Jing(jh2298@cam.ac.uk)
摘要	Mental health plays a key role in everyone's day-to-day lives, impacting our thoughts, behaviors, and emotions. Also, over the past years, given their ubiquitous and affordable characteristics, the use of smartphones and wearable devices has grown rapidly and provided support within all aspects of mental health research and care-from screening and diagnosis to treatment and monitoring-and attained significant progress in improving remote mental health interventions. While there are still many challenges to be tackled in this emerging cross-disciplinary research field, such as data scarcity, lack of personalization, and privacy concerns, it is of primary importance that innovative signal processing and deep learning (DL) techniques are exploited. In particular, recent advances in DL can help provide a key enabling technology for the development of next-generation user-centric mobile mental health applications. In this article, we briefly introduce the basic principles associated with mobile device-based mental health analysis, review the main system components, and highlight the conventional technologies involved. We also describe several major challenges and various DL technologies that have potential for strongly contributing to dealing with these issues, and we discuss other problems to be addressed via research collaboration across multiple disciplines.
DOI	10.1109/MSP.2021.3099293
关键词[WOS]	DEPRESSION
收录类别	SCI
语种	英语
资助项目	Bavarian Ministry of Science and Arts as part of the Bavarian Research Association ForDigitHealth ; National Natural Science Foundation of China[62071330] ; National Natural Science Foundation of China[61702370] ; Key Program of the National Natural Science Foundation of China[61831022]
项目资助者	Bavarian Ministry of Science and Arts as part of the Bavarian Research Association ForDigitHealth ; National Natural Science Foundation of China ; Key Program of the National Natural Science Foundation of China
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000711718500018
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46351
专题	多模态人工智能系统全国重点实验室_智能交互
通讯作者	Han, Jing
作者单位	1.Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB3 0FD, England 2.Imperial Coll London, Dept Comp, London, England 3.Augsburg Univ, Human Ctr Artificial Intelligence, D-86159 Augsburg, Germany 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 5.Tianjin Normal Univ, Comp Sci, Tianjin 300387, Peoples R China 6.Univ Augsburg, Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany 7.Imperial Coll London, Dept Comp, Artificial Intelligence, London SW7 2AZ, England
推荐引用方式 GB/T 7714	Han, Jing,Zhang, Zixing,Mascolo, Cecilia,et al. Deep Learning for Mobile Mental Health: Challenges and recent advances[J]. IEEE SIGNAL PROCESSING MAGAZINE,2021,38(6):96-105.
APA	Han, Jing.,Zhang, Zixing.,Mascolo, Cecilia.,Andre, Elisabeth.,Tao, Jianhua.,...&Schuller, Bjoern W..(2021).Deep Learning for Mobile Mental Health: Challenges and recent advances.IEEE SIGNAL PROCESSING MAGAZINE,38(6),96-105.
MLA	Han, Jing,et al."Deep Learning for Mobile Mental Health: Challenges and recent advances".IEEE SIGNAL PROCESSING MAGAZINE 38.6(2021):96-105.