Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

By David C. Mohr, Mi Zhang, Stephen M. Schueller. Annual Review of Clinical Psychology, 2017

732 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/1d0b94641dadd41d6bb2242c217390697a87923d

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Citations in the corpus

Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, background): “If we leverage today ’ s available technologies, we can obtain continuous, long-term monitoring of the unique bio-psycho-social profiles of individuals [26] that impact their mental health.”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “…light) enables collection of real-world, continuous data concerning symptoms, treatment response, and behaviors, thoughts, and emotions (62,63). a r Platforms designed to enable multimodal data collection, like mindLAMP (64), AWARE (65), and CrossCheck (66) aid in continuous remote…”
Detecting depression and mental illness on social media: an integrative review (2017, background): “However, as depressed users may cease generating social media content [36], alternative uninterrupted data streams such as text messages and sensor data should also be tested for ongoing monitoring applications [37].”
Improve Unsupervised Domain Adaptation with Mixup Training (2020, methodology): “In building HAR system for ﬁtness monitoring and assisted living [17, 18] We conduct experiments on the same WiFi activity recognition dataset [12] used in [6].”
The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “Smartphone delivery also opens up the possibil ity of automated collection of sensor data to derive behavioral markers 80 , which would open up many possibilities for tailored interventions, while also raising a number of privacy and ethical concerns.”
Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety (2019, background): “2,79,80 For example, Mohr and colleagues advocate using digital phenotyping to build markers of behavioral traits and use these, in turn, to explore relationships with higher-level states.”
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing (2016, background): “ mobile devices are able to infer user activities and states [10, 38, 41] and recognize surrounding context [34, 44]. These capabilities serve diverse application areas including health and wellbeing [23, 36, 27], tracking and imaging [13, 22, 28, 47], mobile security [32, 33, 43], and vehicular roa”
AI in mental health. (2020, background): “Digital phenotyping [2] or personal sensing [3] involves using sensor and usage data from personal digital devices, particularly smartphones, to infer contextual and behavioural information about an individual that can then be used as input for machine learning methods to predict…”