# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Rehabilitation Assessment and Auxiliary Training  
The advent of the intelligent era of medical devices has propelled the rapid development of scientific and technological medical diagnosis and evaluation. Currently, an increasing number of lower limb rehabilitation assessment and auxiliary functional training devices based on TENG technology have been reported. Inspired by the skin of sharks,Cheng et al. invented a TENG based on shark skin bionics [72]. It uses liquid metal and Ecoflex as the friction layers (Figure 5(iii)). A gait monitoring system constructed based on the bionic TENG, together with the sensors of four TENGs installed on the soles of the feet and the data acquisition system, jointly constructs a gait analysis system (Figure 5(ai)). [PERSON] et al. divided the gait cycle generated during human walking into four stages (Figure 5(aiii)), and the gait of the participants was analyzed based on the signals generated in these four stages. Notably, the states of the liiopsoas muscle and the tibialis anterior muscle were monitored in real time to assess injury rehabilitation (Figure 5(aiv)). The gait monitoring system can also be used to monitor anterior cruciate ligament (ACL) tears and meniscus injuries (Figure 5(av)). [PERSON] et al. also developed a wearable electronic sensor based on a TENG and applied it to a robot-assisted rehabilitation system (Figure 5(bi)) [73]. The workflow of the robot-assisted rehabilitation system is shown in Figure 5(bi). The robot-assisted rehabilitation system not only includes the intelligent insole for analyzing the gait, but also four intelligent sensing seat belts for monitoring the movement of the waist (Figure 5(biii)), and the system is able to provide patients with individualized training programs that are tailored to their condition (Figure 5(biv)). The TENG based on fabric can be combined with clothing and accessories to realize the monitoring of gait signals. The research by [PERSON] et al. introduced a gait monitoring system of a self-powered multipoint body motion sensing network with an all-textile structure, as shown in Figure 5(c) [74]. The system relies on machine learning technology to analyze the limb movement cycle signals and dynamic indicators in real time, achieving precise monitoring of the user's gait. Meanwhile, they also designed a personalized auxiliary rehabilitation training system (Figure 5(d)). In addition, the system can also customize personalized and targeted rehabilitation plans for patients. Figure 5(e) demonstrates a human-machine interaction platform for patient rehabilitation. In addition, some researchers have developed a TENG sensor that integrates the functions of assessment and correction for pathological gaits. [PERSON] et al. integrated a TENG using a polymer with self-healing and shape memory functions [75]. This polymer-based TENG exhibited excellent output stability and self-healing ability, that is, when the device is broken, the output performance can still recover to the original state after healing (Figure 5(fi)). The healable and shape-memory dual functional polymers triboelectric nanogenerator (HSP-TENG) also had a large shape fixation rate and a shape recovery rate in high-temperature environments (Figure 5(fi)). The contact and separation between the foot and the corrective insole based on the HSP-TENG generates signals. The information provided by these signals can not only enable early detection of biomechanical anomalies associated with flatfoot, but the design of the sensor's shape can also offer certain arch support for patients with flatfoot (Figure 5(iii)). The research by [PERSON] et al. proposed a self-powered wearable multidimensional motion sensor that can sense vertical acceleration and planar angular velocity [76]. The sensor is composed of an acceleration sensor and an angular velocity sensor (Figure 5(gji)). The researchers integrated the sensor into a belt to construct a VR-assisted training system, and the workflow of this system is shown in Figure 5(gji).