JOURNAL ARTICLE

Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment.

  • Published In: Journal of Intelligent Systems & Internet of Things, 2024, v. 13, n. 2. P. 102 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Alkrimi, Jameela Ali; Mahajan, Sulabh; Jaffer, A. Mohamed; Dev, Sudhanshu; V., Akshay Kumar; Shah, Jaymeel 3 of 3

Abstract

The game's physical and physiological stakes are equal for all players. The two dimensions that rely on the power of physical and physiological consequences are the pursuer and the defence. Whether a chaser or defender, male or female, the physiological actions that occur during the physical activity will have a good effect on the body and on the personality. A Wireless Body Area Network (WBAN) is a network that may transmit real-time traffic like data, speech, and video to monitor the state of essential organs capabilities while remaining external to the body. The present research provides a clear evaluation of how different bones and muscles function, metabolism, movement regulation, and energy generation in relation to varying environmental conditions. There are physiological differences between a chaser and a defender. The primary goal is to gain an in-depth IoT based understanding of how several physiological variables, such as resting heart rate, maximum heart rate, aerobic capacity, and the regulation and maintenance of red blood cells and haemoglobin, are affected by skeletal muscle contraction. It was discovered based on artificial intelligence that the defenders with high speed agility and flexibility performed better in the pre-test. Physiological variables have a considerable impact on speed, strength, agility, and flexibility tests. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Intelligent Systems & Internet of Things. 2024/08, Vol. 13, Issue 2, p102
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:2769786X
  • DOI:10.54216/JISIoT.130208
  • Accession Number:179114833
  • Copyright Statement:Copyright of Journal of Intelligent Systems & Internet of Things is the property of American Scientific Publishing Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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