An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications
Why this work is in the frame
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Bibliographic record
Abstract
Individuals with spinal cord injury (SCI) report upper limb function as their top recovery priority. To accurately represent the true impact of new interventions on patient function, evaluation should occur in a natural setting. Wearable cameras can be used to monitor hand function at home, using computer vision to automatically analyze the resulting egocentric videos. A key step in this process, hand detection, is difficult to accomplish robustly and reliably, hindering the deployment of a complete monitoring system in the home and community. We propose an accurate and efficient hand detection method that uses a simple combination of existing detection and tracking algorithms, evaluated on a new hand detection dataset, consisting of 167,622 frames of egocentric videos collected from 17 individuals with SCI in a home simulation laboratory. The F1-scores for the best detector and tracker alone (SSD and Median Flow) were 0.90±0.07 and 0.42±0.18, respectively. The best combination method, in which a detector was used to initialize and reset a tracker, resulted in an F1-score of 0.87±0.07 while being two times faster than the fastest detector. The method proposed here, in combination with wearable cameras, will help clinicians directly measure hand function in a patient's daily life at home.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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