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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

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Related Experiment Video

Updated: May 10, 2026

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
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Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model.

Shunsaku Takigami1, Atsuyuki Inui1, Yutaka Mifune1

  • 1Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, Japan.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
Summary

This study introduces an AI-powered method for accurately measuring shoulder rotation angles, overcoming traditional goniometer limitations. The novel approach combines pose estimation AI and machine learning for precise analysis in sports and rehabilitation.

Keywords:
artificial intelligencerange of motionshoulder

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Area of Science:

  • Biomechanics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional goniometers present challenges in accurately measuring complex shoulder joint movements, particularly internal/external rotation.
  • The intricate base and moving axes of the shoulder complicate angle measurements from an upright position.

Purpose of the Study:

  • To develop and evaluate a novel method for estimating shoulder joint internal/external rotation angles.
  • To leverage pose estimation artificial intelligence (AI) and machine learning to overcome the limitations of traditional measurement techniques.

Main Methods:

  • Utilized pose estimation AI to extract coordinate parameters from shoulder movement videos of 10 healthy volunteers.
  • Trained machine learning models, including linear regression and Light GBM, using these parameters and smartphone angle device measurements as ground truth.
  • Compared the performance of different machine learning models in estimating shoulder rotation angles.

Main Results:

  • The Light GBM model achieved a high correlation coefficient of 0.999 and a low Mean Absolute Error (MAE) of 0.945.
  • The linear regression model yielded a correlation coefficient of 0.971 and an MAE of 5.778.
  • The developed method accurately estimates internal and external rotation angles from a direct-facing view.

Conclusions:

  • The combination of pose estimation AI and machine learning offers a precise and accessible method for measuring shoulder rotation.
  • This AI-driven approach holds significant value for analyzing motor movements in sports performance and physical rehabilitation.
  • The findings suggest a potential advancement in non-invasive joint angle assessment technologies.