Deep Learning for Mobile Mental Health: Challenges and recent advances
文献类型：期刊论文
| 作者 | Han, Jing7; Zhang, Zixing6; Mascolo, Cecilia7; Andre, Elisabeth5; Tao, Jianhua1 ; Zhao, Ziping3,4; Schuller, Bjoern W.2,3
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| 刊名 | IEEE SIGNAL PROCESSING MAGAZINE
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| 出版日期 | 2021-11-01 |
| 卷号 | 38期号:6页码:96-105 |
| ISSN号 | 1053-5888 |
| DOI | 10.1109/MSP.2021.3099293 |
| 通讯作者 | Han, Jing(jh2298@cam.ac.uk) |
| 英文摘要 | 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. |
| WOS关键词 | DEPRESSION |
| 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] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000711718500018 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46350]  |
| 专题 | 模式识别国家重点实验室_智能交互 |
| 通讯作者 | Han, Jing |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Imperial Coll London, Dept Comp, Artificial Intelligence, London SW7 2AZ, England 3.Univ Augsburg, Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany 4.Tianjin Normal Univ, Comp Sci, Tianjin 300387, Peoples R China 5.Augsburg Univ, Human Ctr Artificial Intelligence, D-86159 Augsburg, Germany 6.Imperial Coll London, Dept Comp, London, England 7.Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB3 0FD, 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. |
入库方式： OAI收割
来源：自动化研究所
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