[Retracted] Design and development of the sEMG-based exoskeleton strength enhancer for the legs

Mikecon Cenit; Vaibhav Gandhi; Mikecon Cenit; Vaibhav Gandhi

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1. sEMG pattern recognition based on recurrent neural network
Tarek M. Bittibssi, Abd Haliem Zekry, Mohamed A. Genedy, Shady A. Maged
Biomedical Signal Processing and Control vol: 70 first page: 103048 year: 2021
doi: 10.1016/j.bspc.2021.103048

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About The Authors

Mikecon Cenit
Middlesex University
United Kingdom

Vaibhav Gandhi
https://mdx.ac.uk/about-us/our-people/staff-directory/profile/gandhi-vaibhav

Middlesex University London
United Kingdom

Vaibhav joined the department of Design Engineering & Mathematics, School of Science & Technology, Middlesex University London in 2013, where he is currently a Senior Lecturer in Robotics, Embedded Systems and Real-time Systems. His research interests include brain-computer interfaces, biomedical signal processing, computational intelligence, computational neuroscience, use-centric graphical user interfaces, and assistive robotics. Vaibhav is a Fellow of The Higher Education Academy, Life Member of the Indian Society for Technical Education (ISTE) and is also a Consultant for Doctorate in Professional Studies (DProf).

Vaibhav received a First Class (Dist.) degree in Instrumentation & Control engineering in 2000, a First Class (Dist.) Masters degree in Electrical engineering in 2002 and a Ph.D. degree in Computing & Engineering in 2012. He was a recipient of the UK-India Education & Research Initiative (UKIERI) scholarship for his Ph.D. research in the area of Brain-Computer Interface for assistive robotics carried out at the Intelligent Systems Research Center, University of Ulster, UK and partly at IIT Kanpur, India. His Ph.D. research was focused on quantum mechanics motivated EEG signal processing, and an intelligent adaptive use-centric human-computer interface design for real-time control of a mobile robot for the BCI users. His post-doctoral research involved work on shadow-hand multi-fingered mobile robot control using the EMG/muscle signals, with contributions also in the 3D printing aspects of a robotic hand.

More information (and videos) about his research on Brain-Computer Interfaces for assistive robotics can be found in his book website http://booksite.elsevier.com/9780128015438/index.php.

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