Shoulder Physiotherapy Activity Recognition 9-Axis Dataset
Bibliographic record
Abstract
**Dataset will be uploaded soon - dataset is complete but uploader is currently freezing midway through status bar**This dataset contains inertial data consisting of 1) physiotherapy exercise recordings, and 2) unlabeled other activity data recordings, each collected by smart watches worn by healthy subjects. This dataset may be used to perform supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with the unlabeled other activity data.It 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) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.052 |
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".