Classification of limb movements using different predictive analysis algorithms

P. Uday Ashish (), Rashtra Vibhuti Sharma (), Sindhu Hak Gupta and Asmita Rajawat ()
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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

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 3, No 27, 1385-1395

Abstract: 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.

Keywords: Data mining; Machine learning; Neural networks; Ensemble learning; Random forest; Multilayer perceptron; K-NN; Gaussian Naive Bayes; Stochastic gradient descent (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01484-2

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