An on-skin triboelectric sensor system enabled by heterogeneous parallel channel fusion model for human-machine interactions

Abstract

The highly flexible wearable device for human-machine interaction is a burgeoning technology that has garnered considerable attention among researchers. However, current wearable devices such as data gloves, wrist bands and exoskeleton devices require additional fixation aids, which interfere with the users’ movements and cause discomfort. Here, a flexible on-skin triboelectric sensor is developed for high-precision human-machine interaction, which is in the form of a thin film containing a soft substrate and two triboelectric layers with mismatched elastic modulus. The on-skin triboelectric sensors can be mounted on the forearm epidermis by their own adhesion, which is rarely perceptible and does not impede hand and wrist mobility. On the basis, the sensors can accurately measure the tiny deformations caused by muscle movement. Meanwhile, a heterogeneous parallel channel fusion (HPCF) model is proposed for advanced signal processing and recognition of sensor data, where varying signal features are extracted by different-sized convolutional kernels. The human-machine interaction system by combining the on-skin triboelectric sensors and algorithm achieves up to 99.12% accuracy when identifying 26 distinct gestures, which holds vast potential in several areas, such as gesture recognition, virtual reality, and teleoperation.