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About

Disi Chen is a leading researcher in human-robot interaction, specializing in surface electromyography (sEMG)-based control, gesture recognition, and dexterous robotic manipulation. Their most influential work, "Multi-object intergroup gesture recognition combined with fusion feature and KNN algorithm" (65 citations), pioneered a novel feature extraction method from activated muscle regions, advancing rehabilitation robotics by enabling more intuitive human-computer interfaces. Chen further extended sEMG applications in "Force Estimation Based on sEMG using Wavelet Analysis and Neural Network" (6 citations), improving grip force prediction for seamless human-robot collaboration. In robotic manipulation, Chen developed "Grasping detection of dual manipulators based on Markov decision process with neural network" (18 citations), integrating reinforcement learning for adaptive dual-arm control. Their foundational contributions include "Intelligent Computational Control of Multi-Fingered Dexterous Robotic Hand" (14 citations), which optimized DSP/FPGA controllers for the HIT/DLR II hand, and "Fusion hand gesture segmentation and extraction based on CMOS sensor and 3D sensor" (17 citations), enhancing multimodal gesture recognition. With over 120 total citations, Chen’s work bridges sEMG signal processing, computer vision, and neural network control, driving innovations in assistive robotics and intelligent manufacturing.