Exploring the intersection of equipment design and human physical ability: Leveraging biomechanics, ergonomics/anthropometry, and wearable technology for enhancing human physical performance
Bibliographic record
Abstract
This perspective article highlights the development of sports equipment design and development. The impact of ergonomics/anthropometry and biomechanics on sports equipment design is discussed, focusing on the need for physical enhancement, and accommodating equipment for a diverse range of users. The use of innovative materials in sports equipment design, which has led to increased energy return and power output during sports performance, is also highlighted. Further, the article emphasizes the importance of interdisciplinary collaboration among various academic disciplines to examine past problem-solving approaches, develop new methods, and identify future research directions. Additionally, the article discusses four generations of sports equipment design, each characterized by specific design and engineering approaches that have significantly improved athletic performance. The fourth/current generation is expected to focus on biologically altering or modifying human physical capabilities to enhance athletic performance, with wearable technology identified as a key tool in this endeavor. Overall, the article provides a comprehensive overview of the evolution of sports equipment design and the role of interdisciplinary collaboration in enhancing human physical ability in sports. The article encourages further research in this area, particularly in the use of wearable technology and material innovation to enhance athletic performance in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".