Capturing hand use of individuals with spinal cord injury at home using egocentric video: A feasibility study
Bibliographic record
Abstract
Abstract Background Measuring arm and hand function in the community is a critical unmet need of rehabilitation after cervical spinal cord injury (SCI). This information could provide clinicians and researchers with insight into an individual’s independence and reliance on care. Current techniques for monitoring upper limb function at home, including self-report and accelerometry, lack the necessary resolution to capture the performance of the hand in activities of daily living (ADLs). On the other hand, a wearable (egocentric) camera provides detailed video information about the hand and its interactions with the environment. Egocentric recordings at home have the potential to provide unbiased information captured directly in the user’s own living environment. Purpose To explore the feasibility of capturing egocentric video recordings in the home of individuals with SCI for hand function evaluation. Study Design Feasibility study Methods Three participants with SCI recorded ADLs at home without the presence of a researcher. Information regarding recording characteristics and compliance was obtained as well as structured and semi-structured interviews involving privacy, usefulness and usability. A video processing algorithm capable of detecting interactions between the hand and objects was applied to the home recordings. Results 98.58±1.05 % of the obtained footage was usable and included 4 to 8 unique activities over a span of 3 to 7 days. The interaction detection algorithm yielded an F1-score of 0.75±0.15. Conclusion Capturing ADLs using an egocentric camera in the home environment after SCI is feasible. Considerations regarding privacy, ease of use of the devices and scheduling of recordings are provided.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".