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dc.contributor.advisorHa, Thi Xuan Chi
dc.contributor.authorNguyen, Thi Hoai Lien
dc.date.accessioned2025-02-12T02:01:45Z
dc.date.available2025-02-12T02:01:45Z
dc.date.issued2024
dc.identifier.urihttp://keep.hcmiu.edu.vn:8080/handle/123456789/6367
dc.description.abstractGlobal population growth, aging populations, and higher survival rates for people with severe illnesses and impairments are all factors that are driving up the demand for rehabilitation services throughout the world especially in Low- and Middle-Income Countries (LMICs). Physical therapy, sometimes referred to as physiotherapy, is a branch of medicine that deals with the treatment of patients by physical therapists who use patient rehabilitation to maintain, improve, or restore health. By creating a learning machine model that can identify the proper angle states, machine learning can be applied as a solution for workout movement recognition. Image processing and computer vision activities can be carried out with the help of the Python module OpenCV. Many functions are available to it, including pose tracking, face recognition, and item identification. One benefit of the Mediapipe framework is its ability to identify human posture using 33 points, or landmarks. In order to perform the research, a literature review was done, and depending on the values of the x and y axes, similar research was located. The purpose of this paper is to enhance one's posture when exercising. The implementation of an AI-powered smart system that uses live image and video sensing to recommend improved body posture by using python libraries including Mediapipe, OpenCVen_US
dc.subjectMedia Pipe and OpenCVen_US
dc.titleReal Time Posture Detection Of Motivation Of Physical Therapy For Rehabilitating Process Based On Media Pipe And Opencven_US
dc.typeThesisen_US


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