Bibliographic record
Abstract
This thesis presents a real-time human activity analysis system, where a user’s activity can be quantitatively evaluated with respect to a ground truth recording. Multiple Kinects are used to solve the problem of self-occlusion while performing an activity. The Kinects are placed in locations with different perspectives to extract the optimal joint positions of a user using Singular Value Decomposition (SVD) and Sequential Quadratic Programming (SQP). The extracted joint positions are then fed through our Incremental Dynamic Time Warping (IDTW) algorithm so that an incomplete sequence of an user can be optimally compared against the complete sequence from an expert (ground truth). Furthermore, the user’s performance is communicated through a novel visual feedback system, where colors on the skeleton present the user’s level of performance. Experimental results demonstrate the impact of our system, where through elaborate user testing we show that our IDTW algorithm combined with visual feedback improves the user’s performance quantitatively.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".