ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Advances in computer vision and deep learning are enabling automated environment recognition systems for robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms like convolutional neural networks to recognize the human walking environment. To address these limitations, we developed "ExoNet" - the first open-source, large-scale hierarchical database of wearable camera images (i.e., egocentric perception) of real-world walking environments. Unparalleled in both scale and diversity, ExoNet contains over 5.6 million RGB images of indoor and outdoor walking environments, which were collected using a lightweight wearable camera throughout the summer, fall, and winter. Approximately 923,000 images in ExoNet were human-annotated using a new 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet serves as a communal platform to train, develop, and compare next-generation image classification algorithms for visual perception of human walking environments. In addition to robotic leg prostheses and exoskeletons, applications of ExoNet can extend to humanoids, autonomous legged robots, powered wheelchairs, and other mobility assistive technologies. 

References:

1) Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and AI. DOI: 10.3389/frobt.2020.562061.

2) Laschowski B, McNally W, Wong A, and McPhee J. (2021). Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons. Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). DOI: 10.1109/EMBC46164.2021.9630064. 

3) Laschowski B (2021). Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons. PhD Dissertation. University of Waterloo. http://hdl.handle.net/10012/17816. 

4) Laschowski B, McNally W, Wong A, and McPhee J. (2022). Environment Classification for Robotic Leg Prostheses and Exoskeletons using Deep Convolutional Neural Networks. Frontiers in Neurorobotics. DOI: 10.3389/fnbot.2021.730965.