3‐D pose presentation for training applications
Why this work is in the frame
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Bibliographic record
Abstract
Purpose In the authors' experience, the biggest issue with pose‐based exergames is the difficulty in effectively communicating a three‐dimensional pose to a user to facilitate a thorough understanding for accurate pose replication. The purpose of this paper is to examine options for pose presentation. Design/methodology/approach The authors examine three methods of presentation and feedback to determine which provides the user with the greatest improvement in performance. An on‐body sensor network system was used to measure success rates, and address the challenges and issues that arise throughout the process. Findings A three‐dimensional interface allows for full control of the camera, and after conducting all of the experiments, the importance of this feature became exceedingly apparent. Though other elements of feedback were able to illustrate specific problem areas, the camera rotation improved some success rates by more than double. Research limitations/implications Refinements of visual feedback methods during training could include determining the ideal position for the camera to view the avatar after the rotation to maximize pose comprehension. Future research could also include working towards providing the participant with more specific instructions, verbally or symbolically. Originality/value In a traditional setting, such as a yoga class, a physically present moderator would provide coaching to participants who struggled with pose reproduction. However, for obvious reasons, this cannot be implemented in a computer‐based training setting. This research begins to examine what is the necessary user interface for activities that are traditionally very closely monitored.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it