Flexible microfluidic triboelectric sensor for gesture recognition and information encoding

Flexible triboelectric sensor has a broad application prospects in self-powered human-machine interface. However, due to the complexity of real environment, the output signal is easily disturbed, thus resulting in low reliability. Here, a flexible microfluidic triboelectric sensor (FMTS) was proposed by counting the number of wave peaks in output waveform. With a high transmittance of 82%, the FMTS shows bendable, twistable and conformable characteristics so that can be operated attached to skin. Based on triboelectrification and electrostatic induction between liquid stream, microchannel and interdigital electrodes, it enables to generate quantifiable voltage wave peaks. The FMTS has a maximum sensitivity of 0.418 kPa−1 and a wide detection range from 2.38 to 58.12 kPa with the microchannel width of 500 µm. For demonstration, a FMTS is attached to the finger, and the number of peaks is used to determine the angle of finger bending with a resolution of 10°. The machine learning approach is employed to achieve accurate recognition of five gestures with an accuracy of 99.2%. The information can also be defined by using waveforms generated by different combinations of movements. An encoding system that recognizes eight types of information with an accuracy of up to 98.8% is finally demonstrated.