Self‐Powered Wireless Temperature Monitor System Based on Triboelectric Nanogenerator with Machine Learning

Abstract

Triboelectric nanogenerator (TENG) can power wireless, real‐time sensing system with hybrid electromagnetic or piezoelectric power, or directly drive commercial LED without battery. However, it is a great challenge to directly drive wireless real‐time sensing system due to low energy density based on environment energy. Here, a self‐powered smart wireless temperature monitoring system that uses machine learning to accurately measure the ambient temperature is developed. A position modulation‐based TENG‐driven transmitter enables wireless communication and real‐time temperature monitoring. This machine learning‐based wireless sensor can accurately monitor the ambient temperature, with a recognition accuracy of up to 96.2%. This sensor architecture could potentially be used in low‐cost distributed sensors for environmental monitoring.

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

“…Furthermore, the system can reduce the likelihood of environmental damage by offering predictive insights into changing conditions and enabling stakeholders to implement preventive measures. The combination of AI and TENG not only enhances the efficiency of environmental monitoring but also minimizes the risks associated with human error, ensuring a more reliable and accurate assessment of potential threats. − …”