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Bones are dynamic organs that require a rich supply of oxygen and nutrients. Around 5% to 10% of the cardiac output supplies blood to the bones. A typical long bone has three main sources: the nutrient artery, the metaphyseal and epiphyseal arteries, and the periosteal arteries.
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The nutrient artery is the main blood vessel that enters the diaphysis via the nutrient foramen. While most long bones have only one nutrient foramen, large bones, such as the femur, may have two. This...
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Related Experiment Video

Updated: May 6, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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Pain State Classification of Stiff Knee Joint Using Electromyogram for Robot-Based Post-Fracture Rehabilitation

Yang Zheng1,2, Dimao He1,2, Yuan He3

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Electromyogram (EMG) signals can detect pain levels during knee rehabilitation mobility training. This allows for accurate estimation of the maximum angle position (maxAP) to improve patient recovery after fractures.

Keywords:
electromyogramfracturepainpattern recognitionrange of motion

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Clinical Biomechanics

Background:

  • Knee joint stiffness after fracture limits range of motion (ROM).
  • Optimal mobility training requires flexing the knee to a maximum angle position (maxAP) that induces appropriate pain levels.
  • Current methods for determining maxAP rely on subjective patient pain reports, which can be variable.

Purpose of the Study:

  • To investigate the feasibility of using electromyogram (EMG) activities to detect the maximum angle position (maxAP) during knee rehabilitation.
  • To convert maxAP detection into a binary classification of pain versus painless states.

Main Methods:

  • Experiment I: Recorded EMG signals from knee flexors and extensors in 12 post-fracture patients during routine mobility training.
  • Extracted EMG features to differentiate between pain and painless states.
  • Experiment II: Tested maxAP estimation using machine learning models (SVM, Random Forest) on a knee rehabilitation robot with 7 patients.

Main Results:

  • EMG features showed significant differences between pain and painless states.
  • Support Vector Machine (SVM) achieved 87.90% ± 4.55% accuracy with a 6.5° ± 5.1° bias.
  • Random Forest model achieved 89.10% ± 4.39% accuracy with a 4.5° ± 3.5° bias.

Conclusions:

  • Pain-induced EMG signals can accurately classify pain states for maxAP estimation in post-fracture mobility training.
  • This approach can potentially enhance the application of robotic techniques in fracture rehabilitation.
  • EMG-based pain detection offers an objective measure to guide rehabilitation intensity and improve ROM recovery.