A Systematic Review of Smart Clothing in Sports: possible Applications to Extreme Sports
Bibliographic record
Abstract
Background. Traditional monitoring of athletes during sports has long been hampered by bulky, complicated and tethered systems. In the past decade, this has changed due to the miniaturization of sensors and improvement of systems to store and transmit data. These systems have been integrated into textiles to create ‘smart clothing’ which has been so ubiquitous that a review of the recent literature is crucial for understanding its full potential and potential use in extreme sports. Methods. An electronic data base search was performed from 2003 to April 2019 for full length articles including "Smart" AND "Clothing" OR "Clothing" AND "Sport(s)" written in English with human subjects. Articles were evaluated according to the Newcastle-Ottawa Scale. Results. Twenty-four studies resulted in 18 systems comprised of 22 types of clothing with various capabilities, including: monitoring heart rate, electromyography, respiratory rate, steps, GPS, energy expenditure, posture, body temperature and identifying the activity. Conclusions. Many types of smart clothing from socks and gloves, to pants, shirts and bras are increasingly utilized to monitor sports activity worldwide and gather previously unavailable, yet highly valuable data. This provides a unique opportunity to study athletes during training and competition, potentially providing more effective training and better safety protocols.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".