Inertial characteristics of upper extremity motions in upper extremity stroke rehabilitation based tasks
Bibliographic record
Abstract
Activity counting has demonstrated strong correlations to recovery before and after stroke rehabilitation. However, there are only moderate to poor correlations with movement specific features (such as timing and repetition) that are significant to stroke rehabilitation, allowing room for improvement. This paper explores the physical meaning of an accelerometric based activity count, by using a precise tri-axial accelerometer and tri-axial gyroscope during tasks based on selected activities of daily living (ADLs). The impact of processing algorithms and sensor choice were also considered. Nine healthy participants performed a series of free-world upper extremity movement tasks modelled after ADLs as well as tasks constrained by speed and direction. Raw gyroscope and accelerometer data were linearly regressed with medically graded actigraphy bands for comparison. The results demonstrated that wrist motion during upper extremity tasks had similar distributions of data across all planes and axes of motion. The results also highlighted that processing algorithms based on mean and median epoched data were more sensitive (p <; 0.05) to differences in planes and axes of motion, but that variance based methods presented lower root-mean-square-errors (RMSE) errors when linearly regressed with medically graded technology. The findings from this study help to better understand inertial patterns of upper extremity rehabilitation based tasks and physical interpretations of activity count measures.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".