Journal of Intelligent Systems and Internet of Things

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https://doi.org/10.54216/JISIoT

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Volume 13 , Issue 2 , PP: 102-112, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

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

Jameela Ali Alkrimi 1 * , Sulabh Mahajan 2 , A. Mohamed Jaffer 3 , Sudhanshu Dev 4 , Akshay Kumar V. 5 , Jaymeel Shah 6

  • 1 University of Babylon Computer Science College of Dentistry, University of Babylon, Babylon, Iraq - (Dent.jameela.ali@uobabylon.edu.iq)
  • 2 Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India - (sulabh.mahajan.orp@chitkara.edu.in)
  • 3 Professor, Department of ISME, ATLAS SkillTech University, Mumbai, Maharastra, India - (mohamed.jaffar@atlasuniversity.edu.in)
  • 4 Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India - (sudhanshu.dev.orp@chitkara.edu.in)
  • 5 PG Scholar, Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, Karnataka, India - (jpc222380@jainuniversity.ac.in)
  • 6 Associate Professor, Department of Computer science and Engineering, Faculty of Engineering and Technology, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India - (jaimeel.shah@paruluniversity.ac.in)
  • Doi: https://doi.org/10.54216/JISIoT.130208

    Received: October 16, 2023 Revised: February 18, 2024 Accepted: June 15, 2024
    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.

    Keywords :

    Physical fitness , R.B.C. , ANOVA , IoT , t-value , WBAN

    References

     [1]      D. M. Moshfeghi and M. T. Trese, “Reducing blindness resulting from Retinopathy of prematurity using deep learning,” Ophthalmology, vol. 128, no. 7, pp. 1077-1078, 2021.

     [2]      C. A. Neves and E. D. Tran, “Deep learning automated segmentation of middle skullā€base structures for enhanced navigation,” International Forum of Allergy & Rhinology, vol. 11, no. 12, pp. 1694–1697, 2021.

     [3]      J. Krupa, “GPU coprocessors as a service for deep learning inference in high energy physics,” Machine Learning: Science and Technology, vol. 2, no. 3, 2021.

     [4]      L. Tian and B. M. Hunt, “Deep learning in biomedical optics,” Lasers in Surgery and Medicine, vol. 53, no. 6, pp. 748–775, 2021.

     [5]      S. Shukla, V. Roy and A. Prakash, "Wavelet-Based Empirical Approach to Mitigate the Effect of Motion Artifacts from EEG Signal," 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India, 2020, pp. 323-326, doi: 10.1109/CSNT48778.2020.9115761.

     [6]      S. F. Chang, H. C. Lin, and C. L. Cheng, “The relationship of frailty and hospitalization among older people: evidence from a meta-analysis,” Journal of Nursing Scholarship, vol. 50, no. 4, pp. 383–391, 2018.

     [7]      S. F. Chang, “Frailty is a major related factor for at risk of malnutrition in community-dwelling older adults,” Journal of Nursing Scholarship, vol. 49, no. 1, pp. 63–72, 2017.

     [8]      Arfat Ahmad Khan, Khalid K. Almuzaini, Víctor Daniel Jiménez Macedo, Stephen Ojo, Vinodh Kumar Minchula, Vandana Roy, MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM, Physical Communication, Volume 58, 2023, 102057, ISSN 1874-4907, https://doi.org/10.1016/j.phycom.2023.102057.

     [9]      V. Roy, S. Shukla, “Effective EEG Motion artifacts Removal with KS test Blind Source Separation and Wavelet Transform”, International Journal of Bio-Science and Bio-Technology, 2016,  Vol. 8, No. 5, 2016, pp. 139-154, DOI:10.14257/ijbsbt.2016.8.5.13.

    [10]     P. H. Chen, The Relationship between Health Status and Physical Function on Fall in Community-Dwelling Older Adults, National Defence Medical Centre, Taipei, Taiwan, 2014.

    [11]     H. L. Chin, Y. C. Lin, Y. F. Hsiao et al., “Study on health fitness factors, grip strength and related factors of middle age and elderly residents in southern Taiwan,” Taiwan Geriatrics and Gerontology, vol. 10, pp. 238–253, 2015.

    [12]     E. Dent, J. E. Morley, A. J. Cruz-Jentoft et al., “Physical frailty: ICFSR international clinical practice guidelines for identification and management,” The Journal of Nutrition, Health & Aging, vol. 23, no. 9, pp. 771–787, 2019.

    [13]     S. L. Wang, L. C. Chou, and L. D. Chiang, “Relationship between falls-related factors and skeleton strength in the elderly,” Chinese Journal of Tissue Engineering Research, vol. 11, pp. 1095–1098, 2007.

    [14]     Marrus, N.; Eggebrecht, A.T.; Todorov, A.; Elison, J.T.; Wolff, J.J.; Cole, L.; Gao, W.; Pandey, J.; Shen, M.D.; Swanson, M.; et al. Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers. Cereb. Cortex 2018, 28, 750–763.

    [15]     Fang, H.; Quan, M.; Zhou, T.; Sun, S.; Zhang, J.; Zhang, H.; Cao, Z.; Zhao, G.; Wang, R.; Chen, P. Relationship between Physical Activity and Physical Fitness in Preschool Children: A Cross-Sectional Study. BioMed Res. Int. 2017, 2017, 9314026.


    [16]     Latorre Román, P.; Mora López, D.; Fernández Sánchez, M.; Salas Sánchez, J.; Moriana Coronas, F.; García-Pinillos, F. Test-retest reliability of a field-based physical fitness assessment for children aged 3–6 years. Nutr. Hosp. 2015, 32, 1683–1688.

    [17]     V. Roy, S. Khaparkar and P. Tripathi, "An Effective Identification of Flavor Complaint By Adaptive Analysis of Electroencephalogram (EEG) Signal," 2023 1st International Conference on Innovations in High-Speed Communication and Signal Processing (IHCSP), BHOPAL, India, 2023, pp. 25-28, doi: 10.1109/IHCSP56702.2023.10127108.

    [18]     C. R. Gale, C. Cooper, and A. Aihie Sayer, “Prevalence of frailty and disability: findings from the English longitudinal study of ageing,” Age and Ageing, vol. 44, pp. 162–165, 2014.

    [19]     Ashok Kumar M, Abirami A, Sindhu P, Ashok Kumar V D, Rani V, Modern Medical Innovation on the Preferred Information about the Medicine using AI Technique, Journal of Cognitive Human-Computer Interaction, Vol. 1, No. 1, (2021): 8-17 (Doi:  https://doi.org/10.54216/JCHCI.010102).

    [20]     [20]. Mohammed Hasan Aldulaimi, Ibrahim Najem, Tabarak Ali Abdulhussein, M. H. Ali, Asaad Shakir Hameed, M. Altaee, Hatira Günerhan, Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques, Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 1, (2023): 24-35 (Doi:  https://doi.org/10.54216/JISIoT.090102).

    [21]     [21]. R. W. Bohannon and K. L. Schaubert, “Test-retest reliability of grip-strength measures obtained over a 12-week interval from community-dwelling elders,” Journal of Hand Therapy, vol. 18, no. 4, pp. 426–428, 2005.

    [22]     Mustafa Altaee, Talib A., M. A. Jalil, Ali J., Thamer A. Alalwani, Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm, Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 1, (2023): 36-48 (Doi:  https://doi.org/10.54216/JISIoT.090103).

    [23]     Aditya Sharma, Aditya Vats, Shiv Shankar Dash, Surinder Kaur, Artificial Intelligence enabled virtual sixth sense application for the disabled, Fusion: Practice and Applications, Vol. 1, No. 1, (2020): 32-39 (Doi:  https://doi.org/10.54216/FPA.010104).

    [24]     Shaymaa Adnan Abdulrahma, Abdel-Badeeh M. Salem, An efficient deep belief network for Detection of Coronavirus Disease COVID-19, Fusion: Practice and Applications, Vol. 2, No. 1, (2020): 05-13 (Doi:  https://doi.org/10.54216/FPA.020102).

     

     
    Cite This Article As :
    Ali, Jameela. , Mahajan, Sulabh. , Mohamed, A.. , Dev, Sudhanshu. , Kumar, Akshay. , Shah, Jaymeel. Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2024, pp. 102-112. DOI: https://doi.org/10.54216/JISIoT.130208
    Ali, J. Mahajan, S. Mohamed, A. Dev, S. Kumar, A. Shah, J. (2024). Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment. Journal of Intelligent Systems and Internet of Things, (), 102-112. DOI: https://doi.org/10.54216/JISIoT.130208
    Ali, Jameela. Mahajan, Sulabh. Mohamed, A.. Dev, Sudhanshu. Kumar, Akshay. Shah, Jaymeel. Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment. Journal of Intelligent Systems and Internet of Things , no. (2024): 102-112. DOI: https://doi.org/10.54216/JISIoT.130208
    Ali, J. , Mahajan, S. , Mohamed, A. , Dev, S. , Kumar, A. , Shah, J. (2024) . Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment. Journal of Intelligent Systems and Internet of Things , () , 102-112 . DOI: https://doi.org/10.54216/JISIoT.130208
    Ali J. , Mahajan S. , Mohamed A. , Dev S. , Kumar A. , Shah J. [2024]. Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment. Journal of Intelligent Systems and Internet of Things. (): 102-112. DOI: https://doi.org/10.54216/JISIoT.130208
    Ali, J. Mahajan, S. Mohamed, A. Dev, S. Kumar, A. Shah, J. "Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 102-112, 2024. DOI: https://doi.org/10.54216/JISIoT.130208