Human motion recognition by a shoes-floor triboelectric nanogenerator and its application in fall detection

With the advent of the Internet of Things (IoT), various IoT devices have been widely used, and in recent years, the rapid development of smart sensors as wearable electronic devices has increasingly promoted human -computer integration. Traditional methods using solid-state power supplies suffer from limited battery life, high maintenance costs and environmental pollution. In this work, we propose a simple yet powerful battery-free human motion sensing system that can recognize human motion through a shoes-ground comprised natural triboelectric nanogenerator (TENG), with a conductive PVA-PEDOT:PSS hydrogel as the signal collection component. In addition, we develop an artificial intelligence (AI)-based fall detection system based on the TENG. To this end, we extend TENG with a custom Bluetooth module to transmit the collected signals to the cloud and develop an anomaly detection AI algorithm to detect fall accidents during walking in real time and send instant messages for notification. We experimentally evaluate the proposed TENG fall detection system and conclude that the human sensing-based TENG system has a wide range of potential in wearable electronic devices, and also provides a viable reference for other applications related to human motion detection.

Automated summary

In this work, the authors propose a battery-free human motion sensing system using a shoes-ground comprised natural triboelectric nanogenerator (TENG) and a conductive PVA-PEDOT:PSS hydrogel. They also develop an AI-based fall detection system based on the TENG, which can detect fall accidents during walking in real time and send instant messages for notification. The experimental results demonstrate the potential of the TENG system in wearable electronic devices and provide a viable reference for other applications related to human motion detection.