Hand contour detection in wearable camera video using an adaptive histogram region of interest
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Monitoring hand function at home is needed to better evaluate the effectiveness of rehabilitation interventions. Our objective is to develop wearable computer vision systems for hand function monitoring. The specific aim of this study is to develop an algorithm that can identify hand contours in video from a wearable camera that records the user's point of view, without the need for markers. METHODS: The two-step image processing approach for each frame consists of: (1) Detecting a hand in the image, and choosing one seed point that lies within the hand. This step is based on a priori models of skin colour. (2) Identifying the contour of the region containing the seed point. This is accomplished by adaptively determining, for each frame, the region within a colour histogram that corresponds to hand colours, and backprojecting the image using the reduced histogram. RESULTS: In four test videos relevant to activities of daily living, the hand detector classification accuracy was 88.3%. The contour detection results were compared to manually traced contours in 97 test frames, and the median F-score was 0.86. CONCLUSION: This algorithm will form the basis for a wearable computer-vision system that can monitor and log the interactions of the hand with its environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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