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Gesture-Based Secure Authentication System Using Triboelectric Nanogenerator Sensors.

Doohyun Han¹, Kun Kim², Jaehee Shin¹

¹School of Mechatronics Engineering, Korea University of Technology & Education, Cheonan-si 31253, Republic of Korea.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces a self-powered gesture authentication system using triboelectric nanogenerator (TENG) sensors. The system achieves 98.15% accuracy in recognizing tap and hold gestures, enhancing IoT device security.

Keywords:

flexible electrode gesture classification self-powered system signal processing triboelectric nanogenerator sensor

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Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Triboelectric nanogenerators (TENGs) offer self-powered sensing capabilities suitable for Internet of Things (IoT) applications.
Gesture-based authentication presents a secure and intuitive alternative to traditional input methods.
Existing authentication systems often rely on external power sources and can be vulnerable to security breaches.

Purpose of the Study:

To develop and validate a gesture-based authentication system using TENG sensors.
To evaluate the performance and reliability of TENG sensors for real-time gesture recognition.
To demonstrate the system's potential for enhancing security in smart devices and IoT applications.

Main Methods:

Fabrication and characterization of TENG sensors for gesture detection (tap, double tap, hold).
Evaluation of sensor electrical properties under varying pressure conditions.
Development of a threshold-based classification algorithm for real-time gesture segmentation and feature extraction.
Training and validation of a random forest classifier using time-domain statistical features (five-fold cross-validation).

Main Results:

TENG sensors exhibited a linear relationship between applied force and output voltage, indicating high sensitivity and precision.
The developed algorithm accurately segmented raw sensor signals and extracted relevant features for classification.
The random forest classifier achieved a high authentication accuracy of 98.15%.
The system demonstrated real-time gesture recognition capabilities.

Conclusions:

TENG-based gesture recognition is a feasible and effective method for secure authentication in smart devices.
The proposed system offers a user-friendly, reliable, and self-powered solution for IoT security.
Future work may involve AI-driven signal processing and multi-sensor integration for improved performance.