Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Wannawas:2023:10.1109/ner52421.2023.10123831,
author = {Wannawas, N and Faisal, AA},
doi = {10.1109/ner52421.2023.10123831},
pages = {1--4},
publisher = {IEEE},
title = {Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching},
url = {http://dx.doi.org/10.1109/ner52421.2023.10123831},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Reaching disabilities affect the quality of life. Functional Electrical Stimulation (FES) can restore lost motor functions. Yet, there remain challenges in controlling FES to induce desired movements. Neuromechanical models are valuable tools for developing FES control methods. However, focusing on the upper extremity areas, several existing models are either overly simplified or too computationally demanding for control purposes. Besides the model-related issues, finding a general method for governing the control rules for different tasks and subjects remains an engineering challenge. Here, we present our approach toward FES-based restoration of arm movements to address those fundamental issues in controlling FES. Firstly, we present our surface-FES-oriented neuromechanical models of human arms built using well-accepted, open-source software. The models are designed to capture significant dynamics in FES controls with minimal computational cost. Our models are customisable and can be used for testing different control methods. Secondly, we present the application of reinforcement learning (RL) as a general method for governing the control rules. In combination, our customisable models and RL-based control method open the possibility of delivering customised FES controls for different subjects and settings with minimal engineering intervention. We demonstrate our approach in planar and 3D settings.
AU  - Wannawas,N
AU  - Faisal,AA
DO  - 10.1109/ner52421.2023.10123831
EP  - 4
PB  - IEEE
PY  - 2023///
SN  - 1948-3554
SP  - 1
TI  - Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching
UR  - http://dx.doi.org/10.1109/ner52421.2023.10123831
UR  - https://ieeexplore.ieee.org/document/10123831
ER  -
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