AI-Enabled Wearable and Flexible Electronics for Assessing Full Personal Exposures

Area of Science:

• Environmental Health
• Biomedical Engineering
• Epidemiology

Background:

• The exposome, encompassing all environmental exposures, is increasingly studied at the individual level.
• Accurate personal exposure assessment is vital for health monitoring and epidemiological research.
• Traditional static instruments are limited for capturing dynamic personal exposures.

Purpose of the Study:

• To highlight the significance of personal exposure assessment in environmental health.
• To introduce flexible wearable sensors as a solution for dynamic personal exposure monitoring.
• To discuss the potential of big data and AI in advancing individual-based epidemiology and medical services.

Main Methods:

• Review of current research on exposome and personal exposure assessment.
• Discussion of the advantages of wearable sensors over static instruments.
• Exploration of the role of big data and artificial intelligence (AI) in large-scale exposure assessment.

Main Results:

• Flexible wearable sensors offer a comfortable and user-friendly approach to measuring personal exposures.
• Wearable sensors are well-suited for dynamic and continuous monitoring of personal environmental exposures.
• Integration with big data and AI can enable large-scale personal exposure assessments.

Conclusions:

• Flexible wearable sensors represent a promising technology for personal exposure assessment.
• Technological advancements are paving the way for a shift towards individual-based epidemiological studies.
• The synergy of wearable sensors, big data, and AI can significantly enhance public health monitoring and personalized medicine.