Bibliographic record
Abstract
OBJECTIVE: Cervical spinal cord injury (cSCI) can impair motor function in the upper limbs. Video from wearable cameras (egocentric video) has the potential to provide monitoring of rehabilitation outcomes at home, but methods for automated analysis of this data are needed. Wrist flexion and extension are essential elements to track grasping strategies after cSCI, as they may reflect the use of the tenodesis grasp, a common compensatory strategy. However, there is no established method to evaluate wrist flexion and extension from egocentric video. METHODS: We propose a machine-learning-based approach comprising three steps-hand detection, pose estimation, and arm orientation estimation-to estimate wrist angle data, leading to the detection of tenodesis grasp. RESULTS: The hand detection in conjunction with the pose estimation algorithm correctly located wrist and index finger metacarpophalangeal coordinates in 63% and 76% of 15,319 annotated frames, respectively, extracted from egocentric videos of individuals with cSCI performing activities of daily living in a home simulation laboratory. The arm orientation algorithm had a mean absolute error of 2.76 +/- 0.39 degrees in 12,863 labeled frames. Using these estimates, the presence of a tenodesis grasp was correctly detected in 72% +/- 11% of frames in videos of 6 activities. CONCLUSION: The results provided a clear indication of which participants relied on tenodesis grasp and which did not. SIGNIFICANCE: This paradigm provides the first method that can enable clinicians and researchers to monitor the use of the tenodesis grasp by individuals with cSCI at home, with implications for remote therapeutic guidance.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".