# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Abnormal Gait Detection and Fall Monitoring
(**fi**) Yarn materials and distribution of sensors in the insole. (**fi**i**) Voltage output and stress distribution of the sensors during a standard gait. (**fiii**) Sole stress distribution in two abnormal gaits. is vulnerable to the interference of the surrounding environment. [PERSON] et al. invented a TENG that utilized the interaction between gas and solid to achieve electron transfer [69]. Its working mechanism was realized through the coupling of the triboelectric effect and electrostatic induction, as well as the dynamic migration of charges in the air (Figure 4(di)). Compared with the dense polysiloxane-dimethylglyoxime-based polyurethane (PDPU), the porous PDPU has better structural self-healing ability, surface adhesiveness, and output stability (Figure 4(dii)). It is integrated into shoes to obtain the motion signals of human walking (Figure 4(div)), which can analyze the normal and dragging gait conditions of the human body, and it also has the self-healing ability (Figure 4(diii)). The technology of knitting yarns has also been widely applied in wearable gait monitoring sensing technology. [PERSON] et al. invented a wearable fabric TENG for fall monitoring [70]. The fabric is designed in a 3D diamond pattern to enhance the surface interaction at the contact interface. This fabric TENG also features strong breathability, washability, and durability. [PERSON] et al. placed two TENGs at the positions of the forefoot and the heel of the insole, respectively, and constructed an abnormal gait detection and fall monitoring system (Figure 4(ei)). This system is capable of detecting abnormal gait in patients in real time. Figure 4(eii) shows that patients unconsciously accelerate their walking speed after deviating from their normal stride pattern. At the end of the gait, although they have the intention to move, their feet are unable to move, and characteristic signals appear immediately. When a patient falls, negative signals are generated, and no positive signals are produced until the patient stands up (Figure 4(eiii)); this is important for safety monitoring of older people at home. For instance, when elderly individuals experience a fall and remain incapacitated on the ground, the corresponding signal remains unchanged. Conversely, if the elderly person manages to rise following the fall, a transformation in the signal occurs. By leveraging this differential signal behavior, it can systematically assess and decide whether emergency assistance should be promptly summoned. The abnormal gait detection and fall monitoring system constructed based on this TENG demonstrates its application potential in the field of health monitoring. In addition, [PERSON] et al. developed a textile TENG based on polyvinylidene difluoride (PVDF) and polyamide 66 (PA66) nanofiber coaxial yarns (Figure 4(fi)) [71]. The textile is realized through a coaxial conjugate electrospinning process. During the textile process, a novel conductive gel composed of liquid metal, silver nanowires (AgNWs), and polyvinyl alcohol (PVA) is filled between the conductive wire core and the nanofiber shell. This filling technology is used to address the poor contact between the core and the shell, and, meanwhile, endows the fabric with stronger electrical conductivity and antibacterial properties. Inspired by the research on the TENG woven with this yarn, [PERSON] et al. developed an insole based on plain fabric for gait monitoring [71]. The insole contains a total of 15 pressure sensors (Figure 4(fii)). As the plantar pressure distributions of the out-toeing gait and the in-toeing gait are different, they are abnormal compared with the standard gait signals. Based on this principle, it can be used for the diagnosis of abnormal gaits and the assessment of rehabilitation (Figure 4(fiii)). The application of TENG technology in the field of gait monitoring provides data references for the diagnosis of abnormal gaits and fall monitoring, and solves the problem of achieving accurate analysis of abnormal gaits relying on complex diagnostic instruments.