Open Access

ARTICLE

Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

Mir Mushhood Afsar¹, Shizza Saqib¹, Yazeed Yasin Ghadi², Suliman A. Alsuhibany³, Ahmad Jalal¹, Jeongmin Park^4,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science and Software Engineering, Al Ain University, Al Ain, 15551, UAE
3 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
4 Department of Computer Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do, 15073, Korea

* Corresponding Author: Jeongmin Park. Email:

Computers, Materials & Continua 2022, 73(3), 4763-4777. https://doi.org/10.32604/cmc.2022.028618

Received 14 February 2022; Accepted 05 May 2022; Issue published 28 July 2022

Abstract

Virtual reality is an emerging field in the whole world. The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities. Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games. The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room. To track the human movement, sensors Micro Processor Unit (MPU6050) are used that are connected with Bluetooth modules and Arduino responsible for sending the sensor data to the game. Further, the sensor data is sent to a machine learning model, which detects the game played by the user. The detected game will be operated on human gestures. A publicly available dataset named IM-Sporting Behaviors is initially used, which utilizes triaxial accelerometers attached to the subject’s wrist, knee, and below neck regions to capture important aspects of human motion. The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity. The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%. The proposed system should apply to many systems such as construction, education, offices and the educational sector. Extensive experimentation proved the validity of the proposed system.

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Keywords

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