Shoulder Physiotherapy Activity Recognition 9-Axis Dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.167 |
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 it