Paper Title
Utilization of Support Vector Machine and Histogram of Oriented Gradients in Squat Posture Classification
Article Identifiers
: Authors
Keywords
Squat, Posture, HOG, SVM, Linear Kernel
Abstract
The squat exercise is widely used as a muscle strengthening exercise in regular fitness exercising in the gym and at home on a daily basis. If not performed accurately there can be repercussions of the squat, which can cause physical complications to subjects. This research looks at classifying the bodyweight squat into one correct and three incorrect posture classes. Videos of the four postures have been obtained and image frames have been extracted from them. The Histogram of Oriented Gradients (HOG) descriptor has been applied on each image to extract features. These features and the grey image have both been combined to make the feature set rich. Support Vector Machine (SVM) with the linear kernel, has been utilized for classification and the method has shown a good level of accuracy on our data set and also on the data set used by another research group.
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How To Cite (APA)
Sakuntala Dilhani Wipulasundara (November-2023). Utilization of Support Vector Machine and Histogram of Oriented Gradients in Squat Posture Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), b375-b383. https://ijnrd.org/papers/IJNRD2311149.pdf
Issue
Volume 8 Issue 11, November-2023
Pages : b375-b383
Other Publication Details
Paper Reg. ID: IJNRD_208769
Published Paper Id: IJNRD2311149
Downloads: 000122001
Research Area: Engineering
Author Type: Foreign Author
Country: Colombo 10, Colombo 10, Sri Lanka
Published Paper PDF: https://ijnrd.org/papers/IJNRD2311149.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311149
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