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Research Article |

VMLHST: Development of an Efficient Novel Virtual Reality ML Framework with Haptic Feedbacks for Improving Sports Training Scenarios

Author(s): Madhuri A. Tayal1*, Minal Deshmukh2, Vijaya Pangave3, Manjushri Joshi4, Sulakshana Malwade5 and Shraddha Ovale6

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2,   Special Issue on Mobile Computing assisted by Artificial Intelligent for 5G/6G Radio Communication

Publisher : FOREX Publication

Published : 30 June 2023

e-ISSN : 2347-470X

Page(s) : 601-608

DOI: https://doi.org/10.37391/ijeer.110249

Abstract

This paper presents the development of a novel virtual reality (VR) machine learning (ML) framework that incorporates haptic feedback to improve sports training scenarios. The framework uses You Look Only Once (YoLo) for object detection, and combines it with ensemble learning to analyze the performance of athletes in a simulated environment and provide real-time feedbacks. The system includes haptic feedback devices that are controlled via Grey Wolf Optimization (GWO) to simulate the physical sensation of a real-world sports scenario, allowing athletes to experience the sensation of force, impact, and movements. The proposed system was tested using a group of professional athletes who participated in various sports, including football, basketball, and tennis. The athletes were asked to perform various training scenarios in the virtual environment, and their performance was compared with their real-world performance levels. The results showed that the proposed system improved the athletes' performance significantly, as they were able to receive immediate and accurate feedback on their movements, and the haptic feedback provided a realistic sensory experience that enhanced their trainings. The proposed research has the potential to revolutionize sports training by providing athletes with an efficient and effective way to improve their performance in a set of safe and controlled environments. The system can be customized to suit various sports and training scenarios, and the ML algorithms can be trained on large datasets to improve their accuracy and effectiveness. The incorporation of haptic feedback provides a unique and realistic experience, making the training more engaging and effective under real-time scenarios. The proposed system showcased an accuracy 93.5%, with 3.5% higher precision, and 4.9% higher recall than existing models, which has the potential to enhance athletic performance and revolutionize the way athletes train for different sports.

Keywords: Sports, Training, Haptic, Feedback, Deep, Learning, Scenarios.




Madhuri A. Tayal*, Department of Information Technology, Shri Ramdeobaba college of Engineering and management, Nagpur, Maharashtra, India; Email: tayalma@rknec.edu

Minal Deshmukh, Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India; Email: minal.deshmukh@viit.ac.in

Vijaya Pangave, Department of ECE, MITWPU School of Polytechnic & Skill Development,Pune, Maharashtra, India;Email: vijaya.pangave@mitwpu.edu.in

Manjushri Joshi, Department of ECE, MITWPU School of Polytechnic & Skill Development, Pune, Maharashtra, India;Email: manjushri.joshi@mitwpu.edu.in

Sulakshana Malwade, Department of Computer Engineering, Dr.VishwanathKarad world peace University, Pune, Maharashtra, India; Email: sulakshana.malwade@mitwpu.edu.in

Shraddha Ovale, Department of Computer Engineering, Pimpri Chinchwad College of engineering, Pune, Maharashtra, India; Email: shraddha.ovale@pccoepune.org

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Madhuri A. Tayal, Minal Deshmukh, Vijaya Pangave, Manjushri Joshi, Sulakshana Malwade and Shraddha Ovale (2023), VMLHST: Development of an Efficient Novel Virtual Reality ML Framework with Haptic Feedbacks for Improving Sports Training Scenarios. ijeer 11(2), 601-608. DOI: 10.37391/ijeer.110249.