Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
//

Contact

 

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

 
 
//

Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
//

Location

 

4.08Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Wannawas:2021:10.1109/NER49283.2021.9441354,
author = {Wannawas, N and Subramanian, M and Faisal, A},
doi = {10.1109/NER49283.2021.9441354},
publisher = {IEEE},
title = {Neuromechanics-based deep reinforcement learning of neurostimulation control in FES cycling},
url = {http://dx.doi.org/10.1109/NER49283.2021.9441354},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Functional  Electrical  Stimulation  (FES)  can  re-store  motion  to  a  paralysed’s  person  muscles.  Yet,  control stimulating  many  muscles  to  restore  the  practical  function  of entire limbs is an unsolved problem. Current neurostimulation engineering still relies on 20th Century control approaches and correspondingly  shows  only  modest  results  that  require  daily tinkering to operate at all. Here, we present our state-of-the-art Deep Reinforcement Learning developed for real-time adaptive neurostimulation of paralysed legs for FES cycling. Core to our approach is the integration of a personalised neuromechanical component  into  our  reinforcement  learning  (RL)  framework that allows us to train the model efficiently–without demanding extended  training  sessions  with  the  patient  and  working  out-of-the-box.  Our  neuromechanical  component  includes  merges musculoskeletal models of muscle/tendon function and a multi-state  model  of  muscle  fatigue,  to  render  the  neurostimulation responsive  to  a  paraplegic’s  cyclist  instantaneous  muscle  capacity.  Our  RL  approach  outperforms  PID  and  Fuzzy  Logic controllers in accuracy and performance. Crucially, our system learned  to  stimulate  a  cyclist’s  legs  from  ramping  up  speed  at the start to maintaining a high cadence in steady-state racing as the muscles fatigue. A part of our RL neurostimulation system has  been  successfully  deployed  at  the  Cybathlon  2020  bionic Olympics  in  the  FES  discipline  with  our  paraplegic  cyclist winning  the  Silver  medal  among  9  competing  teams.
AU  - Wannawas,N
AU  - Subramanian,M
AU  - Faisal,A
DO  - 10.1109/NER49283.2021.9441354
PB  - IEEE
PY  - 2021///
TI  - Neuromechanics-based deep reinforcement learning of neurostimulation control in FES cycling
UR  - http://dx.doi.org/10.1109/NER49283.2021.9441354
UR  - https://ieeexplore.ieee.org/document/9441354
UR  - http://hdl.handle.net/10044/1/87611
ER  -
Download