Adaptive Passive Mobile Sensing Using Reinforcement Learning

Cai, Lihua; Boukhechba, Mehdi; Kaur, Navreet; Wu, Congyu; Barnes, Laura E.; Gerber, Matthew S.

doi:10.1109/wowmom.2019.8792967

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…Adaptive mechanisms for sensing user context only when needed are one example of achieving this balance. For instance, Cai, Boukhechba, Kaur, et al (2019) proposed an adaptive sensing framework using reinforcement learning, a subdivision of machine learning that aims to learn a mapping between states (context in our case) and actions (sense or not), that can guide any future action selection in a given state and maximize the long-term cumulative rewards. The goal of this work is to learn an optimal policy of how to sense the context of a given user, reducing the battery drain burden on the user’s device.…”

Section: Generalized Architecture For Personalized Mental Health Intementioning

confidence: 99%

Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care

Boukhechba¹,

Baglione²,

Barnes³

2020

Ergonomics in Design: The Quarterly of Human Factors Applicatio

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Mental illness is widespread in our society, yet remains difficult to treat due to challenges such as stigma and overburdened health care systems. New paradigms are needed for treating mental illness outside the practitioner’s office. We propose a framework to guide the design of mobile sensing systems for personalized mental health interventions. This framework guides researchers in constructing interventions from the ground up through four phases: sensor data collection, digital biomarker extraction, health state detection, and intervention deployment. We highlight how this framework advances research in personalized mHealth and address remaining challenges, such as ground truth fidelity and missing data.

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Section: Generalized Architecture For Personalized Mental Health Intementioning

confidence: 99%