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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality
by
Jalal, Ahmad
, Park, Jeongmin
, Mushhood Afsar, Mir
, Saqib, Shizza
, A. Alsuhibany, Suliman
, Yasin Ghadi, Yazeed
in
Accelerometers
/ Artificial neural networks
/ Bluetooth
/ Distance learning
/ Experimentation
/ Games
/ Human motion
/ Machine learning
/ Microprocessors
/ Sensors
/ Virtual reality
/ Wrist
2022
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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality
by
Jalal, Ahmad
, Park, Jeongmin
, Mushhood Afsar, Mir
, Saqib, Shizza
, A. Alsuhibany, Suliman
, Yasin Ghadi, Yazeed
in
Accelerometers
/ Artificial neural networks
/ Bluetooth
/ Distance learning
/ Experimentation
/ Games
/ Human motion
/ Machine learning
/ Microprocessors
/ Sensors
/ Virtual reality
/ Wrist
2022
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Do you wish to request the book?
Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality
by
Jalal, Ahmad
, Park, Jeongmin
, Mushhood Afsar, Mir
, Saqib, Shizza
, A. Alsuhibany, Suliman
, Yasin Ghadi, Yazeed
in
Accelerometers
/ Artificial neural networks
/ Bluetooth
/ Distance learning
/ Experimentation
/ Games
/ Human motion
/ Machine learning
/ Microprocessors
/ Sensors
/ Virtual reality
/ Wrist
2022
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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality
Journal Article
Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality
2022
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Overview
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.
Publisher
Tech Science Press
Subject
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