Jingpeng Wang

Doctor of Philosophy
Study Completed: 2019
College of Sciences


Thesis Title
Pattern recognition-based real-time myoelectric control for anthropomorphic robotic systems

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As a means of convenient and non-invasive biosignal-based control, myoelectric control identifies human movement intentions from surface electromyography (EMG) signals recorded on muscles to realise intelligent control of robotic systems. The emerging pattern recognition-based myoelectric control has remained an active research topic in laboratories because of insufficient reliability and robustness. Mr Wang investigated feasible and effective EMG signal processing and pattern recognition methods to establish an intelligent, compact and economical biosignal-based robotic control system. He developed a precision surface EMG signal acquisition method, a reliable pattern recognition-based real-time human hand gesture discrimination approach, and an anthropomorphic robotic hand construction methodology. The testing results of the myoelectric-controlled robotic hand system confirmed the effectiveness of the proposed methods for surface EMG acquisition and human hand gesture discrimination.

Dr Liqiong Tang
Professor John Bronlund