Publication:
Enhancing 2D Joints Estimation In Markerless Motion Capture For Improved Tracking Of Spinal Movements

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dc.contributor.authorPauzi, Ainun Syarafana
dc.date.accessioned2026-05-22T07:39:56Z
dc.date.available2026-05-22T07:39:56Z
dc.date.issued2025-09
dc.description.abstractThis research aims to improve the anatomical accuracy of 2D human pose estimation models by enhancing the level of detail in the skeletal representation, particularly for the spine region. The research is guided by two main objectives: (1) to identify which of three widely used deep learning models (OpenPose, MediaPipe BlazePose, or MoveNet) most accurately predicts keypoints by comparing model outputs with Inertial Measurement Unit (IMU) data; and (2) to develop a curve-fitting algorithm using Bezier and B-Spline formulas to create realistic spine curvature based on new spine keypoints.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24285
dc.language.isoen
dc.subjectImage processing
dc.subjectComputer vision
dc.titleEnhancing 2D Joints Estimation In Markerless Motion Capture For Improved Tracking Of Spinal Movements
dc.typeResource Types::text::thesis::master thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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