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.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".