# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Machine Learning-Assisted Identity Recognition  
In recent years, regarding the applied research of TENGs in the field of gait monitoring, apart from monitoring basic gaits, abnormal gaits, and providing auxiliary training, the identity recognition system based on TENG sensing technology has also received extensive attention. The system is realized by integrating the TENG sensing technology with machine learning technology. The most common form is to place the TENG sensor in the fabric to obtain signals and then use various algorithms to identify these signals. [PERSON] et al. designed a TENG with a wearable PVA/acrylic fluorescent layer and developed a triboelectric sensor array that simulates the structure of nerve cell protrusions that achieved effective perception of multidirectional stress [77]. It consists of a composite PVA/acrylic fluorescent layer and a pair of electrodes. One of the electrodes is coated with a PTFE film to generate induced negative charges (Figure 6(ai)). Due to its excellent flexibility, the TENG is integrated into the insole in the form of a 10-sensor array by researchers for gait monitoring (Figure 6(aii)). The insole-based STA can obtain personal information composed of triboelectric signals. By combining it with a deep learning CNN model, an intelligent system for individual identification was constructed. Figure 6(aiii) shows the triboelectric signals generated by different participants while walking. Through the training and learning of signals among different individuals by the CNN model, individual identification can be completed accurately. Its accuracy rate can reach 99.75% (Figure 6(aiiv)). In addition, [PERSON] et al. combined the invented triboelectric sensor with an insole to create an intelligent insole (Figure 6(bi)). The sensor material is composed of Galinstan and silicone rubber. Galinstan is used as the electrode and Ecoflex is used as the negative friction layer [78]. Machine learning technology can identify the subtle differences in triboelectric signals and, thus, reveal information in depth. The study combined the machine learning algorithm of CNN with the intelligent sensing insole to identify individual information. First, the action datasets generated by different individuals are collected. Then, the calculation is carried out through the CNN model. Finally, the signals generated by them can be identified. Furthermore, the researchers optimized the CNN model by evaluating the cross-entropy loss function. By setting two convolutional layers and two pooling layers, the accuracy rate of individual identification ultimately reaches 94.86% (Figure 6(bii)). In addition to combining sensors with insole, some scholars have also explored other methods and successfully integrated them with the human body. [PERSON] et al. invented a flexible and stretchable ring-shaped TENG. By attaching it to the calf, a gait recognition device was constructed [79]. The ring-shaped TENG is made of a rubber tube filled with physiological saline (Figure 6(ci)). When the wearer's muscles contract and relax, the contact state between the skin and the rubber changes, creating a potential difference between the device's electrodes and the ground, thereby enabling the flow of electrons (Figure 6(cii)). The ring-shaped TENG has strong stretchability and is suitable for being worn on multiple parts of the human body. In their study, the sensor was worn on the calf to capture electrical signals, and then identity recognition was carried out based on these electrical signals (Figure 6(ciii)). [PERSON] et al. proposed a triboelectric gait sensing system integrated into a carpet for human activity recognition [80]. The sensor uses two materials, copper and PTFE, as the friction layers to achieve the triboelectric effect. The researchers designed the sensor in the shape of an eight-channel array and integrated it into the carpet. When the participants walk on the carpet, real-time electrical signals can be generated.