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Smart textiles for multimodal wearable sensing using highly stretchable multiplexed optical fiber system.

Arnaldo Leal-Junior1, Leticia Avellar2, Anselmo Frizera2

  • 1Graduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Fernando Ferrari Avenue, Vitória, 29075-910, Brazil. leal-junior.arnaldo@ieee.org.

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Summary

This study introduces a novel smart textile using highly stretchable polymer optical fiber (POF) sensors for multiparameter detection. The developed wearable sensors accurately measure temperature, force, and displacement, enabling activity recognition.

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Area of Science:

  • Materials Science
  • Textile Engineering
  • Sensor Technology

Background:

  • Development of wearable sensors for real-time monitoring is crucial.
  • Existing sensors often lack durability, flexibility, or multiplexing capabilities.
  • Need for integrated, low-cost sensing solutions in smart textiles.

Purpose of the Study:

  • To develop and evaluate a multiparameter, quasi-distributed smart textile.
  • To utilize highly stretchable polymer optical fiber (POF) sensors for sensing applications.
  • To demonstrate the potential for activity detection using the smart textile system.

Main Methods:

  • Fabrication of light polymerization spinning process (LPS-POF) with high stretchability (Young's modulus 15 MPa, elastic limit 17%).
  • Integration of LPS-POF sensors with flexible light-emitting diodes (LEDs) using on-off keying modulation for multiplexing.
  • Embedding sensors and LEDs within a neoprene textile fabric to create a smart textile system.
  • Evaluation of sensor performance for temperature, transverse force, and angular displacement detection.
  • Application of Principal Component Analysis (PCA) and k-means clustering for activity recognition (walking, sitting, squatting).

Main Results:

  • LPS-POF exhibits good thermal stability within the 13-40 °C range.
  • The smart textile system demonstrated high linearity (R²=0.99) and repeatability (<5% deviation) for physical parameter detection.
  • Successful differentiation of basic human activities (walking, sitting, squatting) using PCA and k-means clustering on sensor data.

Conclusions:

  • The developed smart textile based on stretchable POF sensors offers a portable, low-cost, and effective solution for multiparameter sensing.
  • The system shows significant potential for applications in health monitoring, human-computer interaction, and activity recognition.
  • This work paves the way for advanced wearable sensing technologies integrated into everyday fabrics.