A 3D Printing Triboelectric Sensor for Gait Analysis and Virtual Control Based on Human-Computer Interaction and the Internet of Things

Gait is the information that can reflect the state index of the human body, and at the same time, the leg is the organ with the maximum output power of the human body. Effective collection of maximum mechanical power output and gait information can play an important role in sustainable energy acquisition and human health monitoring. In this paper, a 3D printing triboelectric nanogenerator (3D printed TENG) is fabricated by 3D printing technology, it is composited of Poly tetra fluoroethylene (PTFE) film, Nylon film, and 3D printing substrate. Based on the principle of friction electrification and electrostatic induction, it can be used as the equipment for human sustainable mechanical energy collection and gait monitoring. In order to solve the problems of energy collection, gait monitoring, and immersion experience, we conducted the following experiments. Firstly, the problem of sustainable energy recovery and reuse of the human body was solved. Three-dimensionally printed TENG was used to collect human mechanical energy and convert it into electric energy. The capacitor of 2 mu F can be charged to 1.92 V in 20 s. Therefore, 3D printed TENG can be used as a miniature sustainable power supply for microelectronic devices. Then, the gait monitoring software is used to monitor human gait, including the number of steps, the frequency of steps, and the establishment of a personal gait password. This gait password can only identify a specific individual through machine learning. Through remote wireless transmission means, remote real-time information monitoring can be achieved. Finally, we use the Internet of Things to control virtual games through electrical signals and achieve the effect of human-computer interaction. The peak search algorithm is mainly used to detect the extreme points whose amplitude is greater than a certain threshold and the distance is more than 0.1 s. Therefore, this study proposed a 3D printed TENG method to collect human mechanical energy, monitor gait information, and then conduct human-computer interaction, which opened up a multi-dimensional channel for human energy and information interaction.

Automated summary

This study presents a 3D printed triboelectric nanogenerator that can effectively collect human mechanical energy and convert it into electrical energy. By monitoring gait and utilizing machine learning, real-time monitoring of gait information and the establishment of personal gait passwords are achieved. Human-computer interaction is achieved through the control of virtual games via the Internet of Things and electrical signals.