ExoNet Database: Wearable Camera Images of Human Locomotion Environments
Why this work is in the frame
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Bibliographic record
Abstract
Advances in robotic vision and artificial intelligence 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-quality wearable RGB camera images of human locomotion environments. Using a lightweight wearable smartphone camera system, over 5.6 million images of indoor and outdoor real-world walking environments were collected throughout summer, autumn, and winter seasons. Approximately 940,000 images of 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 offers an unprecedented shared platform for training, developing, and comparing next-generation image classification algorithms. Beyond the control of lower-limb exoskeletons and prostheses, applications extend to other assistive technologies (e.g., powered wheelchairs) and humanoids and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. In Preparation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.040 |
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 it