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Background <p>Excessive loading can damage knee cartilage, making it essential to assess and measure joint load effectively. Despite its importance, real-time monitoring of cartilage load in clinical settings remains challenging due to significant technical constraints. Electroarthrography, a recently introduced non-invasive technique, offers a promising solution by detecting load-generated potentials in joint cartilage through surface electrodes. While previous studies have primarily focused on static load applications, such as standing weight shift task or simple isometric contraction, our study explores its potential in dynamic loading scenarios.</p> Methods <p>We analyzed data from 20 knees in 20 subjects, using eight surface electrodes placed around each knee to capture electrical signals during three activities: active knee extension in a seated position, passive range of motion exercise in a decubitus position, and restricted squats. The recorded signals were processed into potential-time graphs, decomposed according to movement states, and analyzed through a deep neural network.</p> Results <p>The results showed that cartilage-generated potentials were significantly higher during active extension compared to passive extension (1.62 mV vs. 0.87 mV; <i>p</i> &lt; 0.05), with the deep neural network achieving an average classification accuracy of 98.77%.</p> Conclusion <p>These findings highlight the feasibility of measuring and classifying cartilage-generated potentials during dynamic physical activities, providing valuable insights into load-related differences. This approach establishes a solid foundation for applications in rehabilitation medicine by facilitating the determination of appropriate exercise intensities, assessing risks associated with daily activities, and classifying physical activities. Further studies focusing on diverse biomechanical conditions will enhance its clinical utility.</p>

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The cartilage-generated bioelectric potentials induced by dynamic joint movement; an exploratory study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月9日

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  • Jae-Hyun Lee,
  • Ye-Seul Jang,
  • Won-Du Chang

摘要

Background

Excessive loading can damage knee cartilage, making it essential to assess and measure joint load effectively. Despite its importance, real-time monitoring of cartilage load in clinical settings remains challenging due to significant technical constraints. Electroarthrography, a recently introduced non-invasive technique, offers a promising solution by detecting load-generated potentials in joint cartilage through surface electrodes. While previous studies have primarily focused on static load applications, such as standing weight shift task or simple isometric contraction, our study explores its potential in dynamic loading scenarios.

Methods

We analyzed data from 20 knees in 20 subjects, using eight surface electrodes placed around each knee to capture electrical signals during three activities: active knee extension in a seated position, passive range of motion exercise in a decubitus position, and restricted squats. The recorded signals were processed into potential-time graphs, decomposed according to movement states, and analyzed through a deep neural network.

Results

The results showed that cartilage-generated potentials were significantly higher during active extension compared to passive extension (1.62 mV vs. 0.87 mV; p < 0.05), with the deep neural network achieving an average classification accuracy of 98.77%.

Conclusion

These findings highlight the feasibility of measuring and classifying cartilage-generated potentials during dynamic physical activities, providing valuable insights into load-related differences. This approach establishes a solid foundation for applications in rehabilitation medicine by facilitating the determination of appropriate exercise intensities, assessing risks associated with daily activities, and classifying physical activities. Further studies focusing on diverse biomechanical conditions will enhance its clinical utility.