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

Maryam Ghassan Majeed^1,*, Hawraa Ali Sabah², Mustafa Nazar Dawood³, Mohaned Adile⁴, Noor Hanoon Haroon⁵, Mariok Jojoal⁶, Ahmed Mollah Khan⁷

¹Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq

²Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq

³Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq:

⁴Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq:

⁵Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi- Qar, Iraq

⁶Department of Computer Science and Engineering, University of Deusto, 48007 Bilbao, Spain;

⁷Department of  Computer Engineering, University of Massachusetts Dartmouth, MA 02747Inst, USA; Email: Maryam .Ghassan@Kunoozu .Edu .Iq; hawraa.a.sabah@nust.edu.iq; ahmed.khan@umassd.edu

mustafanazar1990@gmail.com; Mohaned Adile@uoalfarahidi.edu.iq; noor@alayen.edu.iq; marjoj@@deusto.es; noor@alayen.edu.iq

^*Corresponding Author: Maryam .Ghassan@Kunoozu .Edu .Iq

Abstract

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.

Keywords: Sportsperson; Performance; Health; Wearable Device; Machine Learning