Injury Analysis Framework for Athletes

Zeki ORALHAN; Burcu ORALHAN

Injury Analysis Framework for Athletes

Yazar Zeki ORALHAN
Burcu ORALHAN
Tür Bildiri
Tarih 2023
DOI Numarası YOK
Yayıncı Orclever Science&Research Group
Dergi, konferans, armağan kitap adı 3rd International Conference on Design, Research and Development pp.39 - 39
Tanımlayıcı Adres https://hdl.handle.net/20.500.14440/1572
Konu Başlıkları Yapay Zeki

Sports has emerged as one of the world's major industries. Thus, athlete health is one of the most critical elements influencing the sector's profits and losses. When potential injuries or poor performance are expected, the risks can be reduced by assessing the players' physical health state and performance. Although traditional methods can only provide limited forecasts, machine learning approaches will allow us to take steps to improve athlete performance and avoid potential injury concerns. In this work, modeling was used to forecast athlete injury and poor performance risks using a rule-based expert system technique. The proposed method has been implemented into software. With the model fed on the athletes' simultaneous data, it was able to take immediate action in the event of an injury or poor performance. This reduces the existing hazards for the invested sportsmen and allows them to perform better. In the study, the fuzzy logic approach was used for modeling, and an accuracy rate of 75.2% was obtained.

Koleksiyonlar Fakülteler
İktisadi ve İdari Bilimler Fakültesi
İşletme
Mühendislik Fakültesi
Elektrik ve Elektronik Mühendisliği

Görüntülenme

8

24.01.2024 tarihinden bu yana

İndirme

1

24.01.2024 tarihinden bu yana

Son Erişim Tarihi

16 Mayıs 2024 07:11

Google Kontrol

Tıklayınız

performance injury athlete potential health modeling software athletes Sports implemented method proposed technique system simultaneous reduces perform obtained accuracy approach better allows sportsmen invested hazards existing rule-based action immediate expert influencing reduced expected injuries losses

Eser Adı (dc.title)	Injury Analysis Framework for Athletes
Yazar (dc.contributor.author)	Zeki ORALHAN
Yazar (dc.contributor.author)	Burcu ORALHAN
Tür (dc.type)	Bildiri
Açık Erişim Tarihi (dc.date.available)	2023-12-31
Alt Tür (dc.type.alttur)	Sözel
Alt Tür 1 (dc.type.alttur1)	uluslararası
Dergi, konferans, armağan kitap adı (dc.relation.journal)	3rd International Conference on Design, Research and Development
Yayıncı (dc.publisher)	Orclever Science&Research Group
Tarih (dc.date.issued)	2023
Yayının İlk Sayfa Sayısı (dc.identifier.startpage)	39
Yayının Son Sayfa Sayısı (dc.identifier.endpage)	39
ORCID No (dc.contributor.orcid)	https://orcid.org/0000-0003-2841-6115
Özet (dc.description.abstract)	Sports has emerged as one of the world's major industries. Thus, athlete health is one of the most critical elements influencing the sector's profits and losses. When potential injuries or poor performance are expected, the risks can be reduced by assessing the players' physical health state and performance. Although traditional methods can only provide limited forecasts, machine learning approaches will allow us to take steps to improve athlete performance and avoid potential injury concerns. In this work, modeling was used to forecast athlete injury and poor performance risks using a rule-based expert system technique. The proposed method has been implemented into software. With the model fed on the athletes' simultaneous data, it was able to take immediate action in the event of an injury or poor performance. This reduces the existing hazards for the invested sportsmen and allows them to perform better. In the study, the fuzzy logic approach was used for modeling, and an accuracy rate of 75.2% was obtained.
Dil (dc.language.iso)	İNGİLİZCE
DOI Numarası (dc.identifier.doi)	YOK
Konu Başlıkları (dc.subject)	Yapay Zeki
İsmi Geçen (dc.identifier.ismigecen)	Üniversite ismi geçen
Dizin Platformu (dc.relation.platform)	Google Scholar
WOS Kategorileri (dc.identifier.wos)	Diğer