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Ready-to-wear strain sensing gloves for human motion sensing.

Sara S Mechael1, Yunyun Wu1, Yiting Chen1

  • 1Department of Chemistry and Biochemistry, University of Windsor, Windsor, Ontario N9B 3P4, Canada.

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|June 21, 2021
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Summary

Researchers developed novel soft sensors on disposable nitrile butadiene rubber (NBR) gloves for advanced human augmentation. These wearable sensors effectively map hand gestures, enabling precise gesture differentiation and robotic control applications.

Keywords:
BioelectronicsElectronic engineeringNanotechnology fabricationSensor

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Area of Science:

  • Materials Science
  • Wearable Technology
  • Biomedical Engineering

Background:

  • Integrating soft sensors into wearable platforms is crucial for human augmentation.
  • Fabricating integrated wearable sensors on ready-to-wear platforms is an underdeveloped area.
  • Disposable gloves offer an ideal substrate for mapping hand gestures.

Purpose of the Study:

  • To develop a method for fabricating resistive sensing arrays directly on off-the-shelf nitrile butadiene rubber (NBR) gloves.
  • To investigate the performance of these NBR-based sensors for gesture monitoring and control.

Main Methods:

  • Solution-based metallization was used to create sensing arrays on NBR gloves.
  • The surface roughness of the NBR substrate was leveraged to enhance sensor sensitivity.
  • Sensor performance was evaluated under various strain conditions.

Main Results:

  • NBR sensors exhibited a sheet resistance of 3.1 ± 0.6 Ω/sq.
  • A large linear working range (two linear regions ≤70%) was achieved.
  • High gauge factors were recorded (62 up to 40% strain, 246 from 45-70% strain) due to microcrack formation in the metal film on the rough NBR substrate.

Conclusions:

  • The developed NBR-based soft sensors demonstrate high sensitivity and a wide working range.
  • The fabrication method is suitable for creating wearable sensors on disposable gloves for gesture recognition.
  • These sensors show potential for applications in human augmentation, gesture differentiation, and robotic control.