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Related Concept Videos

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...

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WiFi-Powered Sensor Integrated into a Smart Glove with a Fully Fabric Antenna for the Human-Machine Interface.

Kok-Tong Lee1, Eng-Hock Lim1, Chun-Hui Tan1,2

  • 1Lee Kong Chian Faculty of Engineering and Science (LKC FES), Universiti Tunku Abdul Rahman (UTAR), Selangor 43000, Malaysia.

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PubMed
Summary

This study presents a novel smart glove with yarn-based bend sensors, wirelessly powered by WiFi energy harvesting. This flexible, accurate human-machine interface achieves 98.75% object recognition accuracy for intuitive control.

Keywords:
flexible electronicsgraphenehuman-machine interfaceswearable smart textileswireless-power transferyarn sensors

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Human-Computer Interaction

Background:

  • Traditional glove-based human-machine interfaces (HMIs) are limited by rigid structures and bulky batteries, hindering continuous operation.
  • Flexible sensors are crucial for developing intuitive and effective HMIs, but power delivery and sensor reliability remain challenges.

Purpose of the Study:

  • To develop a wirelessly powered smart glove using yarn-based bend sensors for enhanced human-machine interaction.
  • To demonstrate reliable energy harvesting from WiFi signals and accurate finger-bending detection.
  • To evaluate the system's performance in object recognition using machine learning.

Main Methods:

  • Integration of yarn-based bend sensors into a textile glove.
  • Wireless power harvesting using a 5.8 GHz WiFi-band antenna receiver.
  • Utilizing machine learning for object recognition based on sensor data.
  • Testing sensor performance across various strain frequencies and finger-bending angles.

Main Results:

  • Yarn-based bend sensors achieved a gauge factor of 5.60 for strains up to 10% with consistent performance.
  • The smart glove accurately detected finger-bending movements from 0° to 90°.
  • The system demonstrated 98.75% classification accuracy for object recognition.
  • The microwave-powered system showed no voltage deterioration up to 1 m, overcoming near-field limitations.

Conclusions:

  • The developed microwave-powered smart glove offers a practical and reliable solution for intuitive HMIs.
  • The flexible, wirelessly powered design overcomes limitations of traditional HMIs.
  • This technology enables enhanced degrees of freedom and precision in human finger interaction within virtual environments.