Robust Tracking of the Upper Limb for Functional Stroke Assessment
Bibliographic record
Abstract
We present a robust 3-D parts-based (PB) tracking system designed to follow the upper limb of stroke survivors during desktop activities. This system fits a probabilistic model of the arm to sequences of images taken from multiple angles. The arm model defines shapes and colors of limbs and limb configurations that are more or less likely. We demonstrate that the system is 1) robust to cluttered scenes and temporary occlusions, 2) accurate relative to a commercial motion capture device, and 3) capable of capturing kinematics that correlate with concurrent measures of post-stroke limb function. To evaluate the PB system, the functional motion of seven stroke survivors was measured concurrently with the PB system and a commercial motion capture system. In addition, functional motion was assessed by an expert using the Fugl-Meyer Assessment (FMA) and related to recorded kinematics. Standard deviation of differences in measured elbow angles between systems was 5.7°; deviation in hand velocity estimates was 2.6 cm/s. Several statistics, moreover, correlated strongly with FMA scores. Standard deviation in shoulder velocity had a significant correlation coefficient with FMA score below -0.75 when measured with all systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".