Computer vision, machine learning based monocular biomechanical and security analysis

Kumar, Arun; Sharma, Himanshu; Mathur, Shruti; Sharma, Dimpal; Khandelwal, Girraj; Sharma, Gajanand

doi:10.47974/JDMSC-1741

Open Access Research Article

Computer vision, machine learning based monocular biomechanical and security analysis

Arun Kumardr.arunk.nhce@newhorizonindia.eduDepartment of Electronics and Communication EngineeringBengaluru, KarnatakaNew Horizon College of EngineeringIndiaView full profile → , Himanshu SharmaHimanshu.sharma@jecrcu.edu.inDepartment of Computer Science & EngineeringJaipur, RajasthanJECRC UniversityIndiaView full profile → , Shruti Mathursmsavi@gmail.comDepartment of Computer Science & EngineeringJaipur, RajasthanJECRC UniversityIndiaView full profile → , Dimpal Sharmadimpal286@gmail.comDepartment of Computer Science & EngineeringJaipur, RajasthanJECRC UniversityIndiaView full profile → , Girraj Khandelwalgirraj13@gmail.comDepartment of Computer Science & EngineeringJaipur, RajasthanJECRC UniversityIndiaView full profile → , ^*Gajanand SharmaCorresponding authorGajanan.sharma@gmail.comDepartment of Computer Science & EngineeringJaipur, RajasthanJECRC UniversityIndiaView full profile →

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pp. 685–693Vol. 26Issue 3April 2023DOI: 10.47974/JDMSC-1741 Crossmark XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1741
Pages:: 685–693

Abstract

Modern computer vision technologies have served to bridge the gap between contemporary scientific analysis and machine learning assisted digital processing. Within the field of biomechanics, applied strategies incorporating both conventional and machine assisted means have shown great success in augmenting the observations of electromyogram graphical sensors; albeit within the constraints of specialized, multiple-source arrays. The ongoing study represents an endeavor to utilize several distinct technologies to achieve similar results with the application of a single optical sensor. This paper documents the research, development, and implementation of a monocular feature extraction pipeline, designed for the intended use of supplementing modern athletic biomechanical analysis and the security of the data. We will examine the techniques presented by related works, and how we have implemented these strategies into our framework.

Keywords

Computer vision Machine learning Biomechanics Motion tracking

Subject Classifications

Primary 68T45Secondary 68M25

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