PT  - JOURNAL ARTICLE
AU  - Andrew Garrett Kurbis
AU  - Brokoslaw Laschowski
AU  - Alex Mihailidis
TI  - Stair Recognition for Robotic Exoskeleton Control using Computer Vision and Deep Learning
AID  - 10.1101/2022.04.11.487925
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.04.11.487925
4099  - http://biorxiv.org/content/early/2022/04/12/2022.04.11.487925.short
4100  - http://biorxiv.org/content/early/2022/04/12/2022.04.11.487925.full
AB  - Computer vision can be used in robotic exoskeleton control to improve transitions between different locomotion modes through the prediction of future environmental states. Here we present the development of a large-scale automated stair recognition system powered by convolutional neural networks to recognize indoor and outdoor real-world stair environments. Building on the ExoNet database - the largest and most diverse open-source dataset of wearable camera images of walking environments – we designed a new computer vision dataset, called StairNet, specifically for stair recognition with over 515,000 images. We then developed and optimized an efficient deep learning model for automatic feature engineering and image classification. Our system was able to accurately predict complex stair environments with 98.4% classification accuracy. These promising results present an opportunity to increase the autonomy and safety of human-exoskeleton locomotion for real-world community mobility. Future work will explore the mobile deployment of our automated stair recognition system for onboard real-time inference.Competing Interest StatementThe authors have declared no competing interest.