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Efficient Human Posture Recognition and Assessment in Visual Sensor Systems: An Experimental Study.

Lei Lei1, Haonan Zhang2, Qi Zhang3

  • 1School of Information Engineering, Xi'an University, Xi'an 710065, China.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary

This study introduces a novel architecture for human posture recognition and assessment using visual sensors. The system achieves over 96% accuracy, offering real-time performance and scalability for various exercises.

Keywords:
deep learninghuman posture recognitionposture assessment systemvisual sensor system

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Area of Science:

  • Biomechanics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Traditional manual posture assessment methods suffer from fatigue, experience variability, and inconsistent criteria.
  • Visual sensor systems offer an alternative but face challenges with large-scale implementation.
  • There is a need for robust and scalable systems for accurate human posture recognition and assessment.

Purpose of the Study:

  • To propose and validate a novel architecture for human posture recognition and assessment.
  • To overcome the implementation challenges of visual sensor systems in large-scale applications.
  • To develop a system that offers high accuracy, real-time processing, and scalability.

Main Methods:

  • A four-subsystem architecture was designed: Visual Sensor Subsystem (VSS), Posture Assessment Subsystem (PAS), Control-Display Subsystem, and Storage Management Subsystem.
  • The architecture supports parallel data processing through subsystem cooperation.
  • An experimental testbed was built to implement and verify the proposed architecture.

Main Results:

  • The proposed architecture demonstrated high feasibility and rationality through experimental validation.
  • Evaluations using pull-up and push-up exercises yielded an overall accuracy exceeding 96%.
  • The system exhibited excellent real-time performance and scalability across different assessment scenarios.

Conclusions:

  • The developed architecture effectively addresses the limitations of traditional posture assessment.
  • The system provides a reliable, accurate, and scalable solution for human posture recognition and assessment.
  • The findings support the practical application of this architecture in various exercise and rehabilitation settings.