@article {906,
	title = {ExoNet Database: Wearable Camera Images of Human Locomotion Environments},
	journal = {Frontiers in Robotics and Artificial Intelligence},
	volume = {7},
	year = {2020},
	month = {12/2020},
	abstract = {<p>Advances in computer vision and deep learning are allowing researchers to develop environment recognition systems for robotic exoskeletons and prostheses. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed ExoNet - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million images of indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented shared platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged\&nbsp;robots.\&nbsp;</p>
},
	keywords = {Artificial Intelligence, Biomechatronics, Computer Vision, Deep Learning, Environment Recognition, Exoskeletons, Prosthetics, Rehabilitation Robotics, Wearable Technology},
	doi = {10.3389/frobt.2020.562061},
	url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.562061/full},
	author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee}
}
