  Human-robot walking with prosthetic legs and exoskeletons, especially over
complex terrains such as stairs, remains a significant challenge. Egocentric
vision has the unique potential to detect the walking environment prior to
physical interactions, which can improve transitions to and from stairs. This
motivated us to create the StairNet initiative to support the development of
new deep learning models for visual sensing and recognition of stairs, with an
emphasis on lightweight and efficient neural networks for onboard real-time
inference. In this study, we present an overview of the development of our
large-scale dataset with over 515,000 manually labeled images, as well as our
development of different deep learning models (e.g., 2D and 3D CNN, hybrid CNN
and LSTM, and ViT networks) and training methods (e.g., supervised learning
with temporal data and semi-supervised learning with unlabeled images) using
our new dataset. We consistently achieved high classification accuracy (i.e.,
up to 98.8%) with different designs, offering trade-offs between model accuracy
and size. When deployed on mobile devices with GPU and NPU accelerators, our
deep learning models achieved inference speeds up to 2.8 ms. We also deployed
our models on custom-designed CPU-powered smart glasses. However, limitations
in the embedded hardware yielded slower inference speeds of 1.5 seconds,
presenting a trade-off between human-centered design and performance. Overall,
we showed that StairNet can be an effective platform to develop and study new
visual perception systems for human-robot locomotion with applications in
exoskeleton and prosthetic leg control.
