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Classification of limb movements using different predictive analysis algorithms

Author

Listed:
  • P. Uday Ashish

    (Amity University Uttar Pradesh)

  • Rashtra Vibhuti Sharma

    (Amity University Uttar Pradesh)

  • Sindhu Hak Gupta

    (Amity University Uttar Pradesh)

  • Asmita Rajawat

    (Amity University Uttar Pradesh)

Abstract

Monitoring and preventing diseases and infections are a significant challenge in the current state of our healthcare systems, given how it affects the patient mortality rates. Data analysis can aid in promoting these activities by recognizing risk factors and predicting the occurrence of any disease or infection. Predictive analysis algorithms provide useful tools for processing and analyzing the data. Furthermore, analyzing the body movements can assist in administering various rehabilitation processes and help regain the damaged or deteriorated motor skills of human beings. In this work, with the use of the Scatter Parameters (S11 and S21), we can identify the different human hand movements which are used in the kinesiotherapy process. For this work, a dataset is used, where the Transmission and Reflection coefficients of on-body Wireless Body Area Network (WBAN) antennas for each hand movement are depicted to exhibit unique channel functionalities with respect to frequency. This work focuses on the study of the classification of different limb movements by means of different predictive analysis algorithms. The goal of this work is to analyze and compare the results of these different predictive models to find the most accurate model for the purpose of classification. The classification accuracy of human hand movements comes out to be 85% when classifying using S11 parameters, and an accuracy of 99% when classifying using S21 parameters.

Suggested Citation

  • P. Uday Ashish & Rashtra Vibhuti Sharma & Sindhu Hak Gupta & Asmita Rajawat, 2022. "Classification of limb movements using different predictive analysis algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1385-1395, June.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01484-2
    DOI: 10.1007/s13198-021-01484-2
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    References listed on IDEAS

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    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
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