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Data-Driven Material Selection for Flexible Wearable Sensors Under Environmental Coupling Conditions.

Yanping Lu1, Myun Kim1, Hanwen Zhang1

  • 1Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea.

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Summary

This study introduces an AI framework using extreme gradient boosting (XGBoost) for selecting optimal materials for flexible wearable sensors. The data-driven approach enhances signal stability and comfort for reliable health monitoring.

Keywords:
environmental couplingflexible electronic wearable materialsgreen clothingmaterial selection recommendation modelsensors

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Area of Science:

  • Materials Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Flexible wearable electronics are crucial for health monitoring but face challenges with material stability, comfort, and environmental adaptability.
  • Conventional material selection methods lack the sophistication to address these multifaceted requirements under dynamic conditions.

Purpose of the Study:

  • To develop a data-driven material selection framework for flexible wearable sensors using the extreme gradient boosting (XGBoost) algorithm.
  • To enable intelligent material matching and recommendation by integrating user perception, material properties, and environmental performance.

Main Methods:

  • Implementation of an XGBoost-based model for material selection.
  • Integration of user perception, material physical parameters, and environmental coupling indicators.
  • Experimental validation of the model's recommendation accuracy and material performance.

Main Results:

  • The proposed XGBoost model achieved a 94.5% recommendation accuracy, surpassing conventional methods.
  • Silver nanowires (AgNWs) were identified as a superior material, offering high signal-to-noise ratio, low skin impedance, and sweat resistance.
  • Sensors demonstrated <3% deviation in physiological monitoring and significant improvements in signal-to-noise ratio (68%) and reduced sensitivity decay (75%) in environmental tests.

Conclusions:

  • The XGBoost-based framework effectively supports material selection for flexible wearable sensors.
  • The framework enhances signal reliability and environmental adaptability for complex monitoring scenarios.
  • The findings pave the way for more robust and user-friendly wearable health monitoring devices.