Multivideo Models for Classifying Hand Impairment After Stroke Using Egocentric Video
Bibliographic record
Abstract
OBJECTIVES: After stroke, hand function assessments are used as outcome measures to evaluate new rehabilitation therapies, but do not reflect true performance in natural environments. Wearable (egocentric) cameras provide a way to capture hand function information during activities of daily living (ADLs). However, while clinical assessments involve observing multiple functional tasks, existing deep learning methods developed to analyze hands in egocentric video are only capable of considering single ADLs. This study presents a novel multi-video architecture that processes multiple task videos to make improved estimations about hand impairment. METHODS: An egocentric video dataset of ADLs performed by stroke survivors in a home simulation lab was used to develop single and multi-input video models for binary impairment classification. Using SlowFast as a base feature extractor, late fusion (majority voting, fully-connected network) and intermediate fusion (concatenation, Markov chain) were investigated for building multi-video architectures. RESULTS: Through evaluation with Leave-One-Participant-Out-Cross-Validation, using intermediate concatenation fusion to build multi-video models was found to achieve the best performance out of the fusion techniques. The resulting multi-video model for cropped inputs achieved an F1-score of $0.778\pm 0.129$ and significantly outperformed its single-video counterpart (F1-score of $0.696\pm 0.102$ ). Similarly, the multi-video model for full-frame inputs (F1-score of $0.796\pm 0.102$ ) significantly outperformed its single-video counterpart (F1-score of $0.708\pm 0.099$ ). CONCLUSION: Multi-video architectures are beneficial for estimating hand impairment from egocentric video after stroke. SIGNIFICANCE: The proposed deep learning solution is the first of its kind in multi-video analysis, and opens the door to further applications in automating other multi-observation assessments for clinical use.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".