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Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

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COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines.

V Urovi¹, O Jimenez-Del-Toro², F Dubosson²

¹Accounting and Information Management, University of Maastricht, The Netherlands.

Computers in Biology and Medicine

|December 25, 2016

Summary

This study introduces a new temporal logic framework to analyze continuous wearable sensor data for early Metabolic Syndrome risk detection. This approach enables personalized interventions by interpreting physiological patterns and integrating clinical guidelines.

Keywords:

COMPOSE Event calculus.Metabolic syndrome Temporal reasoning

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Area of Science:

Biomedical Informatics
Artificial Intelligence
Health Informatics

Background:

Metabolic Syndrome is linked to obesity and unhealthy lifestyles, necessitating early risk identification.
Continuous monitoring of physiological parameters offers potential for proactive health assessment.
Existing methods may not fully leverage continuous data for complex condition prediction.

Purpose of the Study:

To develop a novel temporal logic-based framework for reasoning with continuous wearable sensor data.
To identify patients at higher risk for Metabolic Syndrome by interpreting physiological parameters.
To integrate diverse clinical guidelines for a unified patient risk profile.

Main Methods:

Defined temporal patterns for reasoning with continuous physiological data.
Extended the Event Calculus temporal logic formalism with these temporal patterns.
Developed coordination mechanisms for combining multiple clinical guidelines.
Tested the framework using four-day continuous monitoring data from twenty subjects.

Main Results:

The framework successfully reasoned with continuous sensor data.
Temporal patterns facilitated rule specification and knowledge integration.
The approach demonstrated potential for patient-specific risk assessment.
Results were validated against a gold standard.

Conclusions:

The novel temporal logic framework enhances reasoning with continuous physiological data.
This approach allows for tailored interventions and educational materials.
Early detection and management of Metabolic Syndrome risk are facilitated.
Personalized healthcare strategies can be developed based on continuous monitoring.