Mingjia Zhang

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Mingjia Zhang is a leading researcher in intelligent mining technology and human-robot interaction, with a focus on enhancing safety and efficiency in underground coal mining operations. His primary research areas include surface electromyography (sEMG)-based gesture recognition, video-based gesture recognition, and the development of remote-controlled rescue robots for hazardous environments. Zhang’s major contributions involve designing advanced neural network architectures, such as multistream convolutional neural networks (CNNs) and two-stream 3D-ConvNet networks, to accurately interpret operator gestures from forearm sEMG signals and video data. His work enables real-time, intuitive control of inspection and rescue manipulators, significantly improving response times in emergency scenarios. With his most-cited paper, “sEMG-Based Gesture Recognition Method for Coal Mine Inspection Manipulator Using Multistream CNN,” garnering 17 citations since 2023, Zhang’s impact is evident in the growing adoption of his methods for robotic teleoperation. Notably, his research addresses critical challenges in underground environments, where traditional communication methods often fail, making his contributions vital for advancing autonomous and semi-autonomous rescue systems. Zhang’s innovative approaches continue to shape the future of intelligent mining and emergency robotics.