An Artificial Neuromuscular System for Bimodal Human–Machine Interaction
Sunyingyue Geng, Shuangqing Fan, Hangfei Li, Yashuai Qi, Chunhua An, Enxiu Wu, Jie Su, Jing Liu
Abstract
Abstract Neuromuscular system enabled muscle functions are critical for body movements, such as rhythmic motions and other complexed movements. Imparting artificial neuromuscular system to advanced robots and interactive systems can potentially improve their sensorimotor coordination and interactivity. Here, an artificial neuromuscular system is reported to mimic the sensing, processing, and manipulation of neuromuscular information, which consists of a triboelectric nano‐generator (TENG), SnErO x neuromorphic transistors (SENTs), and the signal‐converting system. The synaptic performance of the SENT is optimized to implement multiple operation modes of muscle upon receiving signals from TENG, including muscle contraction, fast/slow muscle fiber shift, conscious/unconscious muscle movements, and transformation. As a proof‐of‐concept demonstration, the artificial neuromuscular system is used to develop contact human–machine interaction (HMI) by decoding the surface electromyogram (sEMG) and non‐contact HMI based on supercapacitive iontronic effect. Importantly, both HMI demonstrate real‐time gesture recognition and robotic manipulation, indicating the potential of developing next‐generation smart electronics that desire multiple interaction patterns.