The research findings of the research group of Prof. Wang Hong from the School of Mechanical Engineering and Automation, NEU were published on Expert Systems with Applications, TOP international journal on AI, with the title of Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models. The first author is graduate student Lu Yanzheng and the corresponding is author Wang Hong. The School of Mechanical Engineering and Automation, NEU was the only signing unit of the paper.
Expert Systems with Applications, TOP journal on AI, computer science, etc., with the impact factor of 8,665 in year 2022, first district of Chinese Academy of Sciences, JCR/Q1, ranks the sixth among AI journals regarding H index on Google Scholar.
The paper has realized closed-loop control of human-machine interaction and established sensor fusion algorithm, control algorithm and other modules by gathering electromyographic signals controlled by human nerves, extracting characteristics of perceptive system data, and planning ectoskeletal walking patterns based on the perceptive and control systems of ectoskeleton.
Over recent years, the research group, funded by national key R&D plan and other programs, aim for the international cutting-edge fields, has carried out studies including brain-like intelligence, brain-computer interface and human-machine interaction and collaboration, and developed the brain-computer interface system and brain-controlled UAV system which are qualified for American patents, etc. Previously, the research group published Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection (the first author Wang Hong) and Brain computer interface system based on indoor semi-autonomous navigation and motor imagery for unmanned aerial vehicle control (the first author Shi Tianwei, corresponding author Wang Hong) on Expert Systems with Applications, announcing the research findings on brain-computer interface technologies.
Fig.1 Human-Machine Interaction Closed-loop System based on Electromyographic Signal
