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

Author:

Tayal Madhuri A.1,Deshmukh Minal2,Pangave Vijaya3,Joshi Manjushri3,Malwade Sulakshana4,Ovale Shraddha5

Affiliation:

1. Department of Information Technology, Shri Ramdeoba college of Engineering and management, Nagpur, Maharashtra, India

2. Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

3. Department of ECE, MITWPU School of Polytechnic & Skill Development, Pune, Maharashtra, India

4. Department of Computer Engineering, Dr. Vishwanath Karad world peace University, Pune, Maharashtra, India

5. Department of Computer Engineering, Pimpri Chinchwad College of engineering, Pune, Maharashtra, India

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.

Publisher

FOREX Publication

Subject

Electrical and Electronic Engineering,Engineering (miscellaneous)

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The Application and Impact of Artificial Intelligence on Sports Performance Improvement: A Systematic Literature Review;2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES);2023-11-23

2. Exploring Explainable AI: Current Trends, Challenges, Techniques and its Applications;Proceedings of the 5th International Conference on Information Management & Machine Intelligence;2023-11-23

3. A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces;International Journal of Electrical and Electronics Research;2023-09-23

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