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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  2/2020 - 15
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Comparison of Classification Algorithms for Detecting Patient Posture in Expandable Tumor Prostheses

KOCAOGLU, S. See more information about KOCAOGLU, S. on SCOPUS See more information about KOCAOGLU, S. on IEEExplore See more information about KOCAOGLU, S. on Web of Science, AKDOGAN, E. See more information about AKDOGAN, E. on SCOPUS See more information about AKDOGAN, E. on SCOPUS See more information about AKDOGAN, E. on Web of Science
 
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Download PDF pdficon (1,489 KB) | Citation | Downloads: 798 | Views: 2,011

Author keywords
biomedical measurement, machine learning, prosthetics, supervised learning, support vector machines

References keywords
recognition(19), posture(13), activity(12), wearable(11), detection(9), sensors(8), comput(8), biomed(8), system(7), human(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 131 - 138
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02015
Web of Science Accession Number: 000537943500015
SCOPUS ID: 85087436149

Abstract
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Autonomous tumor prostheses are extended without the need of a clinic and of a medical supervision. It is necessary to make sure that the patient is not standing before extending these prostheses. This study aims to determine the posture of the patient for expandable tumor prostheses by employing oft-used three machine learning-based classification methods through comparing them all with each other. Patient posture is determined by using accelerometer and gyroscope data from inertial control unit placed in autonomous expandable tumor prosthesis. By using the created dataset, 48 features are extracted. Then, for optimization, with feature selection, the number of features is reduced to 10. The selected features are processed using the decision tree, the k-nearest neighborhood and support vector machine algorithms. These algorithms were compared with each other using machine learning performance parameters. Accuracy, recall, precision and F-score values are calculated and compared. Consequently, support vector machine is determined as the most successful technique. Then, the model is tested on the experimental setup developed within the scope of the study, and the posture is determined. It is found that with this system, in the presence of a load on the prosthesis, it can be accurately detected at a rate of 97.1% (the recall parameter).


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 5,785 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 111 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

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