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Eco-Friendly Corn Silk-Based Triboelectric Nanogenerator Sensor for Automated Human Motion Recognition using

Harwinder Singh1, Harminder Singh1, Ravinder Singh Sawhney2

  • 1Department of Mechanical Engineering, Guru Nanak Dev University, Amritsar, Punjab 143005, India.

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|May 1, 2026
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Summary

Researchers developed a biodegradable sensor from corn silk for human motion detection. This eco-friendly device, combined with machine learning, achieved 98.7% accuracy in recognizing walking, running, and jumping activities.

Keywords:
GUIbiodegradablecorn silkenergy harvestinghuman activity monitoringmachine learningtriboelectric nanogenerator (TENG)

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Area of Science:

  • Materials Science
  • Biomedical Engineering
  • Green Electronics

Background:

  • Human activity monitoring is crucial for healthcare and fitness.
  • There is a growing need for sustainable and biodegradable electronic sensors.
  • Existing sensors often lack eco-friendly materials and self-powering capabilities.

Purpose of the Study:

  • To design and develop a biodegradable triboelectric nanogenerator (TENG) sensor using corn silk.
  • To integrate the TENG sensor with machine learning for intelligent human motion detection.
  • To demonstrate a sustainable and self-powered solution for wearable human activity monitoring.

Main Methods:

  • Utilized corn silk as the primary triboelectric material for a biodegradable TENG sensor.
  • Integrated the TENG sensor with an ensemble machine learning model (Histogram gradient boosting classifier).
  • Developed a graphical user interface for real-time sensor data processing and activity recognition.

Main Results:

  • The corn silk-based TENG sensor generated an output voltage of 101 V and could charge commercial capacitors.
  • The integrated machine learning model achieved a 98.7% classification accuracy for distinguishing between walking, running, and jumping.
  • The sensor was successfully evaluated as a wearable device for autonomous human motion detection.

Conclusions:

  • A novel, biodegradable, and eco-friendly TENG sensor based on corn silk was successfully developed.
  • The combination of the TENG sensor and machine learning offers a highly accurate method for human activity recognition.
  • This technology presents a promising pathway for intelligent, self-powered, and sustainable human motion monitoring systems.