StairNet: visual recognition of stairs for human-robot locomotion.

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 develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments.

Stair Recognition for Robotic Exoskeleton Control using Computer Vision and Deep Learning.

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.

Environment Classification for Robotic Leg Prostheses and Exoskeletons Using Deep Convolutional Neural Networks.

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, the current locomotion mode recognition systems being developed for automated high-level control and decision-making rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e.

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

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.