Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Data Driven Training: Enhancing Athletic Performance through Wearable Technologies and Artificial Intelligence

Veri Odaklı Antrenman: Giyilebilir Teknolojiler ve Yapay Zekâ ile Sporcu Performansının Yükseltilmesi
Authors: DILICAN, Tunay;

Data Driven Training: Enhancing Athletic Performance through Wearable Technologies and Artificial Intelligence

Abstract

Abstract This review examines the increasingly prominent concept of data-driven training in contemporary sports science, discussing the role of wearable technologies and artificial intelligence in enhancing athletic performance. Based on the existing body of published research, the literature indicates substantial advancements in performance assessment, load management, and injury prevention. The large datasets generated through wearable devices can be analysed through AI algorithms, enabling the development of highly individualised training models. However, persistent challenges—such as data security, ethical standards, algorithmic bias, and financial accessibility—continue to limit the widespread adoption of these technologies. The review emphasises the need for future systems to be developed in a more transparent, accessible, and ethically grounded manner. Ultimately, data-driven approaches signal a new era in modern sport, one that extends beyond performance optimisation to encompass sustainability and a more human-centred model of athletic training. Keywords: Artificial intelligence, Performance, Training Öz Bu derleme, son yıllarda spor biliminde giderek önem kazanan veri-odaklı antrenman anlayışını incelemekte; giyilebilir teknolojiler ve yapay zekâ uygulamalarının sporcu performansının geliştirilmesindeki rolünü tartışmaktadır. Yayımlanmış çalışmalar temel alınarak, literatür taraması sonucunda performans ölçümü, yük yönetimi ve sakatlık önleme konularında önemli gelişmeler olduğu görülmüştür. Giyilebilir cihazlar aracılığıyla elde edilen büyük veri setleri, yapay zekâ algoritmalarıyla analiz edilerek bireyselleştirilmiş antrenman modellerinin oluşturulmasına imkân tanımaktadır. Bununla birlikte, veri güvenliği, etik standartlar, algoritmik önyargı ve maliyet gibi sorunlar, teknolojilerin yaygın kullanımını sınırlamaktadır. Çalışma, gelecekte bu teknolojilerin daha şeffaf, erişilebilir ve etik temellere dayalı biçimde geliştirilmesinin gerekliliğini vurgulamaktadır. Sonuç olarak, veri odaklı yaklaşımlar, modern sporun sadece performans optimizasyonu değil, aynı zamanda sürdürülebilirlik ve insan merkezli antrenman anlayışı açısından da yeni bir dönemi temsil etmektedir. Anahtar Kelimeler: Yapay zeka, Performans, Antrenman

Related Organizations
Keywords

Artificial intelligence, Performance, Training

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green