Key result
Hidden Markov Models can forecast physiological measures of stress up to 3 minutes in advance, and Q-Learning can adapt interventions in response to affect changes.
Why the study?
Can Hidden Markov Models and Q-Learning be used to forecast physiological measures of stress and adapt just-in-time interventions?
Can Hidden Markov Models and Q-Learning be used to forecast physiological measures of stress and adapt just-in-time interventions?
Demonstrates the feasibility of using machine learning models on physiological data to forecast stress and adapt just-in-time interventions.
May inform future just-in-time stress tools; leaves open clinical validation before practice adoption.
This paper examines just-in-time adaptive interventions (JITAIs) for stress, a pervasive and affective computing application with significant implications for long-term health and quality of life. We discuss fundamental components needed to enabling JITAIs based for one kind of affect data stress. Chronic stress has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. This paper conducts post-hoc experiments and simulations to demonstrate feasibility of both real-time stress forecasting and stress intervention adaptation and optimization. Using physiological data collected by ten individuals in the natural environment for one week, we show 1) that simple Hidden Markov Models (HMMs) can be used to forecast physiological measures of stress with up to 3 minutes in advance; and 2) Q-Learning (QL) combined with eligibility traces could be used by an affective computing system to adapt and deliver any number and type of interventions in response to changes in affect. Our hope is that this work will take us one step closer to using pervasive devices to assist in the daily management of chronic stress and other affect-related challenges.
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Jaimes et al. (2015) studied Stress (n=10). Hidden Markov Models (HMMs) and Q-Learning (QL) was evaluated on Feasibility of real-time stress forecasting and stress intervention adaptation and optimization. Hidden Markov Models can forecast physiological measures of stress up to 3 minutes in advance, and Q-Learning can adapt interventions in response to affect changes.
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