ExoNet Database: Wearable Camera Images of Human Locomotion Environments
Bibliographic record
Abstract
Abstract: Recent advances in computer vision and deep learning have allowed researchers to develop 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 (e.g., convolutional neural networks) for recognizing human walking environments. To address these limitations, we developed "ExoNet" - the first open-source, large-scale hierarchical database of high-resolution wearable camera images (i.e., egocentric perception) of legged locomotion environments. Unparalleled in both 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 throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a novel, 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. In addition to robotic leg prostheses and exoskeletons, applications of ExoNet could 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, 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. Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). DOI: 10.1109/EMBC46164.2021.9630064. 3) Laschowski B, McNally W, Wong A, and McPhee J. (2021). Environment Classification for Robotic Leg Prostheses and Exoskeletons using Deep Convolutional Neural Networks. Frontiers in Neurorobotics. DOI: 10.3389/fnbot.2021.730965.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".