Key result
A wearable device using nonparametric Bayesian models and hidden Markov states successfully quantified physiological changes induced by varying stress levels during a two-week study.
An algorithmic framework using nonparametric Bayesian models can process wearable sensor data to identify real-time physiological states and quantify stress responses.
Supports wearable stress monitoring feasibility; leaves open larger prospective validation before clinical adoption.
The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.
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Chow et al. (2014) studied Physiological stress states. Personalized wellness monitors with algorithmic framework was evaluated on Quantification of physiological changes induced by different environments with various levels of stress. A wearable device using nonparametric Bayesian models and hidden Markov states successfully quantified physiological changes induced by varying stress levels during a two-week study.
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