Michael Pritchard

Michael Pritchard
Aston University · Department of Computer Sciences

Master of Engineering

About

5
Publications
733
Reads
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22
Citations
Additional affiliations
September 2019 - January 2020
Aston University
Position
  • Technician
August 2017 - August 2018
AVL UK
Position
  • Software & Controls Intern
Education
September 2015 - July 2019
Aston University
Field of study
  • Electrical & Electronic Engineering

Publications

Publications (5)
Chapter
Full-text available
In this work, we achieve up to 92% classification accuracy of electromyographic data between five gestures in pseudo-real-time. Most current state-of-the-art methods in electromyographical signal processing are unable to classify real-time data in a post-learning environment, that is, after the model is trained and results are analysed. In this wor...
Conference Paper
Full-text available
In this work we achieve up to 92% classification accuracy of electromyographic data between five gestures in pseudo-real-time. Most current state-of-the-art methods in electromyography signal processing are unable to classify real-time data in a post-learning environment, that is, after the model is trained and results are analysed. In this work we...
Article
Full-text available
Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and with a scarcity of training samples. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Those can r...
Article
Full-text available
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as par...
Preprint
Full-text available
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as par...

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