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.

中文翻译：

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面向移动心理健康的深度学习：挑战和最新进展

心理健康在每个人的日常生活中都起着关键作用，影响着我们的思想、行为和情绪。此外，在过去几年中，鉴于智能手机和可穿戴设备的普遍性和可负担性，它们的使用迅速增长，并在心理健康研究和护理的各个方面（从筛查和诊断到治疗和监测）提供支持，并取得了重大进展在改善远程心理健康干预方面。虽然在这个新兴的跨学科研究领域仍有许多挑战需要解决，例如数据稀缺、缺乏个性化和隐私问题，但最重要的是利用创新的信号处理和深度学习 (DL) 技术。特别是，DL 的最新进展有助于为下一代以用户为中心的移动心理健康应用程序的开发提供关键的支持技术。在本文中，我们将简要介绍与基于移动设备的心理健康分析相关的基本原理，回顾主要系统组件，并重点介绍所涉及的常规技术。我们还描述了几个主要挑战和各种 DL 技术，它们有可能对处理这些问题做出巨大贡献，我们还讨论了通过跨学科的研究合作来解决的其他问题。并强调所涉及的常规技术。我们还描述了几个主要挑战和各种 DL 技术，它们有可能对处理这些问题做出巨大贡献，我们还讨论了通过跨学科的研究合作来解决的其他问题。并强调所涉及的常规技术。我们还描述了几个主要挑战和各种 DL 技术，它们有可能对处理这些问题做出巨大贡献，我们还讨论了通过跨学科的研究合作来解决的其他问题。