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

Area of Science:

• Biomedical Engineering
• Orthopedics
• Signal Processing

Background:

• Assessing knee joint load is crucial for preventing cartilage damage, but real-time clinical monitoring faces technical hurdles.
• Electroarthrography (EAG) is a novel non-invasive method using surface electrodes to detect load-generated potentials in cartilage.
• Existing EAG research primarily examines static loading, limiting its application in dynamic scenarios.

Purpose of the Study:

• To explore the feasibility of electroarthrography for measuring cartilage-generated potentials during dynamic knee loading.
• To assess the ability of a deep neural network to classify different dynamic activities based on EAG signals.

Main Methods:

• Eight surface electrodes recorded electrical signals from 20 knees during active extension, passive range of motion, and squats.
• Signals were processed into potential-time graphs and analyzed using a deep neural network.
• Movement states were decomposed and classified based on the recorded electrical potentials.

Main Results:

• Cartilage-generated potentials were significantly higher during active knee extension (1.62 mV) compared to passive extension (0.87 mV; p < 0.05).
• The deep neural network achieved a high classification accuracy of 98.77% for differentiating movement states.
• The study demonstrated successful measurement and classification of cartilage potentials during dynamic activities.

Conclusions:

• Electroarthrography can effectively measure and classify cartilage-generated potentials during dynamic physical activities.
• This non-invasive approach provides insights into load-related differences, supporting applications in rehabilitation.
• Findings establish a foundation for determining exercise intensity, assessing daily activity risks, and classifying physical activities.