Self-powered gait pattern-based identity recognition by a soft and stretchable triboelectric band

Abstract Since each individual has distinct gait characteristics, monitoring human motion can enable identity recognition. Here, we report a self-powered band that can recognize human identity through gait pattern which is achieved by detecting muscle activity. The self-powered band is a soft and stretchable triboelectric nanogenerator (TENG) that is biocompatible and low-cost, which is looped around human body parts and generates electrical outputs in response to body motions involving muscle activities. The band can quantitatively detect walking step, speed and distance. Furthermore, the detected unique motion pattern of each individual allows the band to be used for identity recognition such as personal computer login and employee clock in through gait monitoring and analysis. This work opens new frontiers for the development of self-powered electronics and inspires new thoughts in human-machine interface.