# 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  
During the process of human walking gait, long-term out-toeing and in-toeing in the gait may cause damage to the human body. Therefore, the detection of abnormal gaits, such as out-toeing and in-toeing, is of great significance for human health monitoring. Currently, numerous TENG-based pressure sensors are used for abnormal gait monitoring. [PERSON] et al. fabricated a TENG with synergistic complementarity and installed six arrays of complementary nanopattern triboelectric nanogenerators (CN-TENGs) on the sole of the shoe to construct a gait monitoring sensing system for diagnosing the out-toeing and in-toeing in the gait (Figure 4(ai,aii)) [66]. When the sole exhibits in-toeing, the electrical signal generated on the inner side is greater than that on the outer side. Conversely, when the sole exhibits out-toeing, the electrical signal on the outer side of the sole is greater than that on the inner side (Figure 4(aiii)). Similarly, [PERSON] et al. developed a self-powered sensing inside to identify abnormal gait by dynamically capturing the changes in plantar stress [67]. The insole contains four TENG sensing units based on the single-electrode mode. The structure of the sensors and their distribution in the insole are shown in Figure 3(bi). [PERSON] et al. used a 16-channel signal acquisition system to diagnose six common abnormal gaits (Figure 3(bi,biii)). In addition, TENG-based strain sensors have shown great potential for abnormal gait monitoring. [PERSON] et al. fabricated a hydrogel-based strain TENG using graphene oxide and polyacrylamide hydrogel (GO-PAM) [68]. Its output power is 2.2 times that of pure PAM hydrogel. [PERSON] et al. integrated the strain TENG into an insole for abnormal gait detection. As shown in Figure 4(ci), the sensors on the sole of the foot acquire signals that are converted and then transmitted from the MCU to a personal computer. Subsequently, the raw signals undergo a fast Fourier transform. Finally, the ANN, decision tree, and random forest models are constructed to recognize and analyze the gaits. [PERSON] et al. successfully applied three machine learning models to identify three types of pathological gaits (Figure 4(cii)). Most of the existing studies on TENGs for gait monitoring are mainly based on the interactions between solid-solid, solid-liquid, and liquid-liquid. The mechanism of these studies is the contact and separation of two independent interfaces, and this mechanism  
Figure 4: Applications of self-powered sensors based on TENG in the field of abnormal gait detection and fall monitoring. (**a**) A sensing system for monitoring in-toeing gait and out-toeing gait. (**ai**) Schematic diagram of complementary nanostructures. (**aii**) Distribution of the array on the sole. (**aiii**) Comparison of signal patterns between in-toeing gait and out-toeing gait. (**b**) Triboelectric sensors for diagnosing six types of abnormal gaits. (**bi**) Design of the insole and the materials and structures of the sensor. (**bii**) Characteristic signals generated by six common abnormal gait patterns. (**biii**) Gait analysis and diagnosis interface. (**c**) Sensing system for diagnosing the gait of Parkinson’s and hemiplegic patients. (**ci**) Schematic diagram of the process for diagnosing and identifying gaits. (**cii**) Voltage signals of Parkinson’s patients and left- and right-hemiplegic patients collected by real-time monitoring. (**d**) Triboelectric sensors for abnormal gait detection. (**di**) Schematic diagram of the structure of a porous TENG with gas-solid interaction. (**dii**) Electrical output of the porous TENG. (**diii**) Schematic diagram of self-healing ability. (**div**) Triboelectric sensors for gait recognition. (**e**) Abnormal gait and fall detection. (**ei**) Schematic diagram of the structure of the TENG. (**eiii**) Signal monitoring of the walking pattern of [PERSON]’s patients. (**eiii**) Fall signal monitoring. (**f**) Application of yarn-woven TENGs in abnormal gait detection.