Shoulder Physiotherapy Activity Recognition 9-Axis Dataset
Bibliographic record
Abstract
Consists of 9-axis inertial sensor data (accelerometer, gyroscope, and magnetometer) collected using a Huawei Watch 2 from 20 healthy subjects (40 shoulders), as they perform 10 shoulder physiotherapy exercises. This dataset also includes ~3 hours of unlabeled other activity data for each patient that can be used to simulate out-of-distribution data (label 11).Dataset is labeled as follows:0: None1: External Rotation (Isometric)2: Scapula Retraction at 90 Degrees Flexion3: Internal Rotation (Isometric)4: Extension (Isometric)5: Abduction (Isometric)6: External Rotation at 0 Degrees Flexion7: Cross Chest Adduction8: Active Flexion9: Shoulder Girdle Stabilization with Elevation10: Triceps Pull Downs11: OODThe subjects repeat each activity 20 times on each side (left and right). Isometric exercises are low-motion stability exercises.
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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) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.039 |
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".