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{Subramanian:2021:10.1109/NER49283.2021.9441218,
author = {Subramanian, M and Park, S and Orlov, P and Shafti, A and Faisal, A},
doi = {10.1109/NER49283.2021.9441218},
publisher = {IEEE},
title = {Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform},
url = {http://dx.doi.org/10.1109/NER49283.2021.9441218},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We   have   pioneered   the   Where-You-Look-Is Where-You-Go  approach  to  controlling  mobility  platforms  by decoding how the user looks at the environment to understand where  they  want  to  navigate  their  mobility  device.  However, many   natural   eye-movements   are   not   relevant   for   action intention  decoding, only  some are,  which  places  a  challenge on decoding, the so-called Midas Touch Problem. Here, we present a   new   solution,   consisting   of   1.   deep   computer   vision   to understand what object a user is looking at in their field of view, with  2.  an  analysis  of  where  on  the  object’s  bounding  box  the user is looking, to 3. use a simple machine learning classifier to determine  whether  the  overt  visual  attention  on  the  object  is predictive of a navigation intention to that object. Our decoding system ultimately determines whether the user wants to drive to e.g., a door or just looks at it. Crucially, we find that when users look at an object and imagine they were moving towards it, the resulting  eye-movements  from  this  motor  imagery  (akin  to neural  interfaces)  remain  decodable.  Once  a  driving  intention and  thus  also  the  location  is  detected  our  system  instructs  our autonomous  wheelchair platform,  the  A. Eye-Drive,  to  navigate to the desired object while avoiding static and moving obstacles. Thus, for navigation purposes, we have realised a cognitive-level human  interface,  as  it  requires  the  user  only  to  cognitively interact  with  the  desired  goal,  not  to  continuously  steer  their wheelchair to the target (low-level human interfacing).
AU  - Subramanian,M
AU  - Park,S
AU  - Orlov,P
AU  - Shafti,A
AU  - Faisal,A
DO  - 10.1109/NER49283.2021.9441218
PB  - IEEE
PY  - 2021///
TI  - Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform
UR  - http://dx.doi.org/10.1109/NER49283.2021.9441218
UR  - https://ieeexplore.ieee.org/document/9441218
UR  - http://hdl.handle.net/10044/1/87612
ER  -
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