Athlete’s Performance Analysis Based on Improved Machine Learning Approach on Wearable Devices

aspg-18-2084-1792028115 20261015013515 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2023 8 1 Athlete’s Performance Analysis Based on Improved Machine Learning Approach on Wearable Devices Maryam Ghassan Majeed Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq Hawraa Ali Sabah Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Mustafa Nazar Dawood Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq Mohaned Adile Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Noor Hanoon Haroon Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi- Qar, Iraq Mariok Jojoal Department of Computer Science and Engineering, University of Deusto, 48007 Bilbao, Spain Ahmed Mollah Khan Department of Computer Engineering, University of Massachusetts Dartmouth, MA 02747Inst, USA 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. 2023 75 91 2084 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.080108 https://americaspg.com/journal/18/article/2084 https://americaspg.com/storage/81694905283.pdf