Noise-Optimized Signal Processing for TENG-Based Touch Sensing Using I2C Integrated Circuits
Moger G. Kakim A. Mubarak A. Bushanov Y. Kalimuldina G. Yeshmukhametov A.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Sensors Journal
2025#25Issue 1323700 - 23709 pp.
Triboelectric nanogenerator (TENG) sensors efficiently convert mechanical stimuli into electrical signals, making them well-suited for touch sensing due to their high sensitivity, durability, and self-powered operation. However, conventional signal acquisition methods introduce noise, affecting measurement accuracy. This article presents a compact, inter-integrated circuit (I2C)-based signal processing circuit designed to enhance signal fidelity while minimizing noise interference. The system’s performance was evaluated by analyzing sensor response across varying contact areas (50 to 576 mm2) and drop heights, demonstrating a consistently high signal-to-noise ratio (SNR) across impact conditions. A high-frequency impact experiment further confirmed the PCB’s ability to distinguish sensor output from noise. Comparison with direct analog acquisition showed a significant enhancement in SNR, validating the PCB’s effectiveness in noise reduction. The system exhibited a rise time of 3.27 ms, ensuring rapid detection of mechanical stimuli. Additionally, the measured latency of 88.9 ms between impact and signal detection falls within acceptable limits for real-time applications. These results establish the proposed PCB-based signal processing method as a practical solution for improving TENG sensor performance in touch-sensing applications requiring high accuracy and noise resilience. The compact, low-power design further enhances its suitability for integration into wearable electronics, interactive surfaces, and space-constrained systems where conventional bulky processing units are impractical.
Integrated circuits , inter-integrated circuit (I2C) protocol , noise filtering , noise reduction , sensor optimization , signal processing , signal-to-noise ratio (SNR) , touch sensing , triboelectric nanogenerators (TENGs)
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Nazarbayev University, Department of Robotics Engineering, Astana, 010000, Kazakhstan
Nazarbayev University, School of Engineering and Digital Sciences, Department of Mechanical and Aerospace Engineering, Astana, 010000, Kazakhstan
Mirai Technovation Ltd., Astana, 010000, Kazakhstan
Nazarbayev University, Department of Robotics Engineering, Advanced Robotics and Mechatronics System Laboratory, Institute of Smart Systems and Artificial Intelligence, Astana, 010000, Kazakhstan
Nazarbayev University
Nazarbayev University
Mirai Technovation Ltd.
Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
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