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Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons

View ORCID ProfileBrokoslaw Laschowski, William McNally, Alexander Wong, View ORCID ProfileJohn McPhee
doi: https://doi.org/10.1101/2021.04.02.438126
Brokoslaw Laschowski
1Waterloo Artificial Intelligence Institute and Department of Systems Design Engineering at the University of Waterloo, Waterloo, ON, Canada
Roles: Student Member, IEEE
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  • For correspondence: brock.laschowski@hotmail.com
William McNally
2Waterloo Artificial Intelligence Institute and the Department of Systems Design Engineering at the University of Waterloo, Waterloo, ON, Canada
Roles: Student Member, IEEE
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Alexander Wong
3Waterloo Artificial Intelligence Institute and the Department of Systems Design Engineering at the University of Waterloo, Waterloo, ON, Canada
Roles: Senior Member, IEEE
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John McPhee
4Waterloo Artificial Intelligence Institute and the Department of Systems Design Engineering at the University of Waterloo, Waterloo, ON, Canada
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Abstract

Robotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73% image classification accuracy. While these preliminary results benchmark Efficient-NetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • * Research supported by the Natural Sciences and Engineering Research Council of Canada (NSERC); the Waterloo Engineering Excellence PhD Fellowship; John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics; and Alexander Wong’s Tier II Canada Research Chair in Artificial Intelligence and Medical Imaging.

  • (email: blaschow{at}uwaterloo.ca).

  • (email: wmcnally{at}uwaterloo.ca).

  • (email: alexander.wong{at}uwaterloo.ca).

  • (email: mcphee{at}uwaterloo.ca).

  • https://ieee-dataport.org/open-access/exonet-database-wearable-camera-images-human-locomotion-environments

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 04, 2021.
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Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons
Brokoslaw Laschowski, William McNally, Alexander Wong, John McPhee
bioRxiv 2021.04.02.438126; doi: https://doi.org/10.1101/2021.04.02.438126
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Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons
Brokoslaw Laschowski, William McNally, Alexander Wong, John McPhee
bioRxiv 2021.04.02.438126; doi: https://doi.org/10.1101/2021.04.02.438126

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