Volume 8 , Issue 1 , PP: 75-91, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Maryam Ghassan Majeed 1 * , Hawraa Ali Sabah 2 , Mustafa Nazar Dawood 3 , Mohaned Adile 4 , Noor Hanoon Haroon 5 , Mariok Jojoal 6 , Ahmed Mollah Khan 7
Doi: https://doi.org/10.54216/JISIoT.080108
Today, every nation strives for international recognition in a variety of sports. Governments invest in games and sports to raise the performance of their teams and athletes to get notoriety. Numerous people are involved in sports execution, including team management, coaches, and biomechanists who monitor athlete fitness and work to achieve remarkable results. Performance analysis is greatly aided by technological integration in sports management. The performance analysis of athletes is evaluated in this research using an upgraded machine learning approach on Improved Machine Learning approach on Wearable Devices (IMLA-WD). This design strategy utilizes wearable devices to collect health data, which is then fed into a machine-learning model to monitor athletes' progress. The athletes' performance is evaluated using standard machine learning methods, and the deep neural network monitors their health status. With a health prediction accuracy of 98.65%, the statistical findings of the proposed model demonstrate the highest performance compared to existing methodologies.
Sportsperson , Performance , Health , Wearable Device , Machine Learning
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