Self-powered motion state monitoring system based on combined triboelectric nanogenerators for human physiological signal monitoring and energy collection

The study of self-powered motion state monitoring systems based on triboelectric nanogenerators has recently received increasing attention. In this paper, we propose a self-powered system consisting of two low-cost and simply manufactured triboelectric nanogenerators for human physiological signal monitoring and energy collection. This system can monitor the trunk information and gait information during human activities, and measure human motion status in a holistic manner. The triboelectric nanogenerator, which monitors body torso information (B-TENG), we optimize its triboelectric layer through the microstructure of sandpaper to increase its contact area with the skin. In addition, by adding iron powder into the B-TENG electrode layer, the magnetic permeability of the induction electrode is increased to improve its output performance, and its maximum open-circuit voltage can reach 44.3 V. The triboelectric nanogenerator, which is installed on the foot to monitor gait information (F-TENG), can reach an average open-circuit voltage of 205.6 V by adding a rectangular protrusion structure to the triboelectric layer. In addition, due to its high output performance (∼4700 μw), the F-TENG can collect mechanical energy generated from the soles of the feet during daily human activities and charge a 100 μF capacitor to 1.4 V within 60 s, subsequently powering the miniature electronics. During foot walking, F-TENG is able to light up more than 60 light-emitting diodes. By having the experimenters wear these two triboelectric nanogenerators, we can record the frequency and amplitude signals of the experimenter's elbow, knee, breath, and gait in real time. We also monitored three running states of the human body through these two triboelectric nanogenerators, including normal state, insufficient exercise capacity, and dyspnea. We believe that this work provides a new direction for the development of big data motion analysis and self-powered smart exercise devices.