Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning.

ABSTRACT: The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining

SUBMITTER: Ghadi Y 

PROVIDER: S-EPMC8627229 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature