A Framework for Interactive Sport Training Technology
Bibliographic record
Abstract
Participation in organized sport has many physical, mental, and social benefits, but there are a variety of obstacles in joining and continued participation including access to adequate coaching, equipment, and training facilities. These obstacles lead to inequitable access to high-quality and engaging training, which is a critical problem because adequate training is the main gateway to learning and participating in a sport. Increasingly, technology is used to augment sports training by improving its effectiveness, accessibility, and/or making it more engaging. However, the vast and disparate number of fields contributing to these advancements make it difficult to comprehensively understand technology’s current and potential impact on sport training. This review synthesizes work across fields, including human-computer interaction, computer science, sport science, engineering, psychology, and health sciences, into a classification of research and findings regarding interactive sport training technology organized around four characterizing dimensions: (1) Why augment sport? (the goal); (2) Which sport skill is being supported? (the target); (3) Which training method is used? (the method); (4) How is training augmented (the form). From this synthesis, we identify gaps in training technology research and propose a framework that can provide a common base for the design and creation of future interactive technologies for sport training.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".