# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Comparison Between TENGs Based on Different Materials and Structure  
In the field of gait monitoring, TENGs with various structures and designs each present a unique set of advantages and disadvantages. TENGs integrated into insoles or soles can monitor multiple parameters and sensitively analyze gait cycles, but they require high material performance and are complicated to install and maintain. TENGs integrated into socks are easy to wear and enable continuous and stable gait monitoring, but there are deficiencies in signal acquisition and mechanical support. Furthermore, non-contact TENGs break through the contact limitation and simplify the design of the system, but they make it difficult to acquire fine gait parameters and are susceptible to environmental interference. Other TENGs with special structural design, such as those based on hydrogels, TENGs with self-healing properties and TENGs made of specific fabrics, have unique advantages, such as adjustable performance and high-power output, but they generally face the problems of difficult research and development, complicated preparation processes, limited application scenarios, or insufficient stability. The distinct characteristics of various TENG structural configurations, material compositions, and design architectures in terms of output performance and application suitability are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Structure** & **Working Mode** & **Tribo-Materials** & **TENG Output** & **Detection Things** & **Refs.** \\ \hline Nanofiber & C-S & PVDF/BaTiO\({}_{3}\)/MWCNT & \(\sim\)374 V, & Step frequency, step speed and & [61] \\  & & and silver fabric & \(\sim\)10.2 mA & acceleration monitoring. & [62] \\ \hline Polymer & TENG + PENG & Al and PFF & \(\sim\)131 V, & Motion status and step & [62] \\  & & & \(\sim\)1.1 \(\mu\)A & frequency monitoring. & [63] \\ \hline Elastomer & S-E & Rubber and carbon & \(\sim\)91 V, & Abnormal gait detection. & [64] \\  & & nanofiber & \(\sim\)2.87 \(\mu\)A & Abnormal gait detection. & [64] \\ \hline Textile & TENG + PENG & PEDOT: PSS coated & \(\sim\)1.71 mW & Gait pattern recognition, & [65] \\  & & fabrics and PTFE & \(\sim\)1.71 mW & motion tracking. & [65] \\ \hline Non-contact & S-E & Body and paper & \(\sim\)2 V & Motion speed tracking. & [66] \\ \hline Copolymer & C-S & Cu and Teflon & \(\sim\)442 V, & Abnormal gait monitoring. & [67] \\  & & & \(\sim\)22.2 \(\mu\)A & Abnormal gait recognition. & [67] \\ \hline Elastomer & Gas–solid interaction & PDPU porous & \(\sim\)5.2 V & Parkinsonian gait recognition. & [68] \\  & & elastomer and air & & & \\ \hline Textile & C-S & Nylon/cotton yarn & \(\sim\)763 V, & Abnormal gait monitoring fall & [70] \\  & and PDMS/BaTiO\({}_{3}\) & \(\sim\)20.4 \(\mu\)A & monitoring. & [71] \\ \hline Yarn-woven & C-S & PVDF and PA66 & \(\sim\)3.45 V, & Abnormal gait detection and & [72] \\  & & yarns & \(\sim\)62.1 mW & joint movement monitoring. & [72] \\ \hline Bionic & S-E & Ecoflex and liquid & \(\sim\)32 V, & Detection of neuromuscular & \\  & & metals & \(\sim\)19.04 mW & disorders and rehabilitation & [73] \\ \hline Textile & C-S & Ecoflex and Ni-fabric & \(\sim\)20 V, & User recognition. & \\  & & & \(\sim\)1.3 \(\mu\)W & Rehabilitation monitoring. & [74] \\ \hline Textile & C-S & \begin{tabular}{c} PE sheet yarn and \\ conductive fabric \\ \end{tabular} & \(\sim\)9 V & Biological gait recognition, & \\  & & & assisted rehabilitation training. & [75] \\ \hline Polymer & S-E & \
\begin{tabular}{c} PBA polymer and \\ movable object \\ \end{tabular} & &
\begin{tabular}{c} Flatfoot treatment. \\ Gait analysis. \\ \end{tabular} & [76] \\ \hline Triboelectric & S-E & Rubber tube and skin & \(\sim\)89.4 V & Identity recognition. & \\  & & & Step frequency and speed & [78] \\  & & & monitoring. & \\ \hline Stacked & C-S & PTFE and copper & \(\sim\)80 V, & Identity recognition. & [80] \\ structures & & foils & \(\sim\)10.58 \(\mu\)W & Fitness monitoring. & [81] \\ \hline \end{tabular}
\end{table}