Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

中文翻译：

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个人感知：使用无处不在的传感器和机器学习来了解心理健康。

日常设备（例如我们的手机，可穿戴设备和计算机）中的传感器会留下一连串的数字痕迹。个人感知是指从嵌入到日常生活中的传感器中收集和分析数据，以识别人类的行为，思想，感觉和特质。本文对与心理健康相关的个人感知研究进行了重要的回顾，主要针对智能手机，但也包括对可穿戴设备，社交媒体和计算机的研究。我们提供了一个分层的分层模型，用于将原始传感器数据转换为与心理健康相关的行为和状态的标记。还讨论了研究方法和挑战，包括隐私和维度问题。尽管个人感知仍处于起步阶段，