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Machine Learning and Deep Learning for Myoelectric Control


   Faculty of Engineering and Physical Sciences


About the Project

Characteristics of surface electromyography (sEMG) signals are highly correlated with neuromuscular activation during muscle contractions. In past decades, this property has been fully exploited in human-machine interfaces (HMI), such as intelligent prostheses, to improve the life quality of disabled people by reconstructing lost functions of upper-limb. A large number of commercial prostheses still utilise conventional control schemes such as on/off control and finite state machine, which are simple and robust, but the number of degrees of freedom (DoFs) that can be actuated are very limited. This project aims to develop machine learning (ML) and deep learning (DL) techniques alogrithms to decode human biomechanics from surface electromyography signals. 


Funding Notes

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