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  1. Home
  3. A Method For Human Pose Estimation And Joint Angle Computation Through Deep Learning.
  1. Home
  3. A Method For Human Pose Estimation And Joint Angle Computation Through Deep Learning.

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Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
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A Method for Human Pose Estimation and Joint Angle Computation Through Deep Learning.

Ludovica Ciardiello1, Patrizia Agnello2, Marta Petyx2

  • 1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.

Journal of Imaging
|April 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a deep learning method for human pose estimation and joint angle calculation, aiding physiotherapy and telemedicine. The approach enables automated motion analysis for digital health and remote patient care.

Keywords:
HPEangle computationartificial intelligencedeep learninghuman pose estimationobject detection

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Area of Science:

  • Computer Vision
  • Medical Imaging
  • Biomechanical Analysis

Background:

  • Human pose estimation is vital for healthcare, sports, and remote monitoring.
  • Accurate joint angle computation is essential for analyzing body posture and alignment in physiotherapy.

Purpose of the Study:

  • To develop a deep learning method for automatic human pose estimation and joint angle computation.
  • To tailor the method for physiotherapy and telemedicine applications, enabling motion analysis.
  • To evaluate the method's effectiveness in real-world use cases for exercise and posture assessment.

Main Methods:

  • A deep learning approach utilizing a customized 25-anatomical keypoint skeleton.
  • Training on a large dataset of over 150,000 annotated and augmented images from open-source datasets.
  • Implementation for both keypoint localization and object detection.

    • Main Results:

    • Achieved a mean Average Precision (mAP@50) of 0.58 for keypoint localization.
    • Achieved a mAP@50 of 0.98 for object detection.
    • Demonstrated practical use cases in evaluating exercise correctness and identifying postural deviations.

    Conclusions:

    • The proposed method offers a promising approach for automated motion analysis in digital health.
    • It has potential impact on rehabilitation support and remote patient care through accurate pose and angle computation.
    • The method effectively supports physiotherapy by analyzing exercise correctness and postural deviations.