Assessing the suitability of Kinect for measuring the impact of a week-long Feldenkrais method workshop on pianists’ posture and movement
Bibliographic record
Abstract
Abstract The Microsoft Kinect depth sensor could offer a convenient, markerless solution for quantifying the head and torso movements of pianists to examine the impact of somatic training on playing postures and movement. To assess the suitability of the Kinect for this application, we tracked four professional piano teachers performing scales immediately before and after a week-long workshop involving daily Feldenkrais Awareness through Movement (ATM) lessons. We compared Kinect skeletal tracking data with 2D reference data obtained simultaneously using Dartfish video analysis software. Analysis revealed frequent tracking errors in the Kinect data compared to reference data from Dartfish. Differences in pre- and post-test measurements of forward head position, head height, C7 vertebra height and shoulder displacement did not correspond between Dartfish and Kinect. Our results suggest that one Kinect sensor does not provide enough accuracy to track torso movements of pianists for the purposes of ergonomic assessment in response to somatic training.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".