Deep Learning for Mobile Mental Health: Challenges and recent advances
IEEE Signal Processing Magazine
Institute of Electrical and Electronics Engineers (IEEE)
MetadataShow full item record
Han, J., Zhang, Z., Mascolo, C., Andre, E., Tao, J., Zhao, Z., & Schuller, B. (2021). Deep Learning for Mobile Mental Health: Challenges and recent advances. IEEE Signal Processing Magazine, 38 (6), 96-105. https://doi.org/10.1109/MSP.2021.3099293
Mental health plays a key role in everyone’s day-to-day lives, impacting our thoughts, behaviours, and emotions. Also, over the past years, given its 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, spanning from screening and diagnosis to treatment and monitoring, and attained significant progress to improve remote mental health interventions. While there are still many challenges to be tackled in this emerging cross-discipline research field, such as data scarcity, lack of personalisation, and privacy concerns, it is of primary importance that innovative signal processing and deep learning techniques are exploited. Particularly, recent advances in deep learning can help provide the key enabling technology for the development of the next-generation user-centric mobile mental health applications. In this article, we first brief basic principles associated with mobile device-based mental health analysis, review the main system components, and highlight conventional technologies involved. Next, we describe several major challenges and various deep learning technologies that have potentials for a strong contribution in dealing with these challenges, respectively. Finally, we discuss other remaining problems which need to be addressed via research collaboration across multiple disciplines.
Mental Health, Bioengineering, Behavioral and Social Science, Mental health, 3 Good Health and Well Being
This paper has been partially funded by the Bavarian Ministry of Science and Arts as part of the Bavarian Research Association ForDigitHealth, the National Natural Science Foundation of China (Grant No. 62071330, 61702370), and the Key Program of the National Natural Science Foundation of China (Grant No: 61831022).
External DOI: https://doi.org/10.1109/MSP.2021.3099293
This record's URL: https://www.repository.cam.ac.uk/handle/1810/326019
All rights reserved
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: email@example.com