ExoNet Database: Wearable Camera Images of Human Locomotion Environments
- Citation Author(s):
- Submitted by:
- Brokoslaw Laschowski
- Last updated:
- Mon, 05/31/2021 - 05:46
Abstract: Advances in computer vision and deep learning are allowing researchers to develop environment recognition systems for robotic leg prostheses and exoskeletons. 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 RGB images of indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system during 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 robots.
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, 7, 562061. 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. bioRxiv. DOI: 10.1101/2021.04.02.438126.
*Details on the ExoNet database are provided in the references above. Please email Brokoslaw Laschowski (firstname.lastname@example.org) for any additional questions and/or technical assistance.