Bibliographic record
Abstract
Background Upper limb rehabilitation after cervical spinal cord injury is vital for regaining independence. Hand function assessments are critical for upper limb rehabilitation, but are unable to accurately reflect performance in the individual’s home environment. Video from wearable cameras (egocentric video), paired with deep learning, can assess hand function during activities of daily living (ADLs) at home. Previous studies have focused on analyzing quantitative aspects of hand usage, but there has yet to be a study that uses deep neural networks to assess the quality of hand usage from egocentric video. Objective To train a deep neural network to predict hand function assessment scores from egocentric video. Methods The dataset used contained egocentric videos of ADLs performed by 17 participants with AIS grades from A-D in a home simulation laboratory. Tasks were annotated with scores adapted from the Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP). The annotated video was then used to train and validate a SlowFast neural network to predict GRASSP scores, using leave-one-subject-out cross validation. Model performance was evaluated by mean absolute error, accuracy, and F1 score. The model was optimized with a hyperparameter sweep. Results The top performing model demonstrated a mean absolute error of 0.52±0.19, an accuracy of 0.55±0.14, and F1 score of 0.55±0.16, on an ordinal scale from 1 to 5. Conclusion These results demonstrate that automated assessment of hand function is possible by applying deep learning to egocentric video. Future work should expand the model to larger datasets with more variability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.875 | 0.765 |
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; the direct Gemma label and the distilled Codex classifier 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".