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Related Concept Videos

Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Syntactic model-based human body 3D reconstruction and event classification via association based features mining and

Yazeed Ghadi1, Israr Akhter2, Mohammed Alarfaj3

  • 1Department of Computer Science and Software Engineering, Al Ain University, Al Ain, UAE.

Peerj. Computer Science
|December 13, 2021
PubMed
Summary

This study introduces a robust method for human posture analysis and gait event detection using complex video data. The approach achieves high accuracy in landmark detection and gait recognition, outperforming existing frameworks.

Keywords:
2D to 3D reconstructionConvolutional neural networkGait event classificationHuman posture analysisLandmark detectionSilhouette optimizationSynthetic model

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Area of Science:

  • Computer Vision
  • Biomechanical Engineering
  • Human-Computer Interaction

Background:

  • Human posture and gait analysis are crucial for applications like healthcare and human-computer interaction.
  • Existing methods often struggle with complex video data and accurate gait event detection.

Purpose of the Study:

  • To develop a robust approach for human posture analysis and gait event detection from complex video data.
  • To improve the accuracy and efficiency of human motion analysis.

Main Methods:

  • Extraction of posture information, landmark data, and 2D skeleton meshes to reconstruct 3D human models.
  • Extraction of contextual features including degrees of freedom, joint angles, motion periodicity, and direction flow.
  • Application of rule-based feature mining and deep learning (CNN) for gait event detection and classification on MPII-VideoPose, COCO, and PoseTrack datasets.

Main Results:

  • Achieved high mean accuracy for human landmark detection (up to 87.72%) and gait event recognition (up to 90.90%) across multiple datasets.
  • Demonstrated significant performance improvements compared to current state-of-the-art methods.
  • Validated the robustness of the proposed system on diverse video-based human motion datasets.

Conclusions:

  • The proposed method offers a significant advancement in human posture analysis and gait event detection.
  • The system's high accuracy and robustness make it suitable for various real-world applications.
  • This research contributes to the development of advanced human life log technologies.