A Battery-Free Unintentional-Fall Detection System Utilizing TENG Insoles

The increasing elderly population has made falls due to frailty a critical issue, with physical and cognitive factors interacting to elevate the risks of fractures and solitary deaths. Falls are challenging to predict due to the involvement of multiple factors, necessitating the development of continuous gait monitoring and fall detection technologies. Although various fall detection methods have been proposed, many rely on batteries requiring maintenance with circuit complexity, higher costs, or both. This study aims to develop an unintentional-falling detection system by utilizing triboelectric nanogenerator (TENG) technology to create a battery-free insole device. The device was tested to analyze the feature of the voltage signals produced by unintentional falls. The results suggest that the signals generated by the device are sufficiently distinguishable from the frequency-amplitude and the ratio of maximum and minimum voltage values during one gait cycle, indicating the feasibility of constructing a battery-free system capable of detecting unintentional-falling.