ExoNet Database: Wearable Camera Images of Human Locomotion Environments
Bibliographic record
Abstract
Advances in artificial intelligence and robotic vision have enabled researchers to develop environment recognition systems for lower-limb exoskeletons and prostheses. However, insufficient and private training datasets have impeded the widespread development and dissemination of image classification algorithms. To address these shortcomings, we have developed the ExoNet Database, the first open-source database of high-resolution wearable RGB camera images of human locomotion environments. Using a lightweight wearable smartphone camera system, over 5.6 million images of outdoor and indoor real-world walking environments were collected throughout the summer, autumn, and winter seasons. Approximately 940,000 images of the ExoNet Database were human-annotated using a 12-classs hierarchical image classification architecture. Images were uploaded to IEEE DataPort and are publicly available for download. The ExoNet Database provides an unprecedented community platform for training, developing, and comparing next-generation image classification algorithms (e.g., convolutional neural networks) for control of lower-limb exoskeletons and prostheses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.020 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".