Towards Real-Time Estimation of Muscle-Fiber Conduction Velocity Using Delay-Locked Loop

Area of Science:

• Biomedical Engineering
• Neuroscience
• Sports Science

Background:

• Muscle fatigue is often indicated by a decrease in muscle-fiber conduction velocity (MFCV).
• Current methods for MFCV estimation from surface electromyography (EMG) require computationally intensive calculations and signal windowing, hindering real-time applications.
• Existing techniques like cross-correlation (CC) and maximum likelihood (ML) have limitations for continuous fatigue monitoring.

Purpose of the Study:

• To develop an adaptive scheme for real-time estimation of muscle-fiber conduction velocity (MFCV).
• To enable continuous monitoring of muscle fatigue using surface electromyography (EMG).
• To reduce the computational complexity associated with MFCV estimation.

Main Methods:

• An adaptive scheme based on a delay-locked loop (DLL) was proposed for MFCV estimation.
• A second-order loop was utilized to track variations in delay over time.
• An error filter was employed to approximate maximum likelihood (ML) estimation under colored noise conditions.
• The DLL system was extended for multichannel conduction velocity (CV) estimation.

Main Results:

• The proposed adaptive DLL method achieved accuracy comparable to the ML method.
• The computational complexity of the proposed method was significantly lower (1/40th) than existing techniques.
• The method demonstrated suitability for real-time MFCV measurements.

Conclusions:

• The developed adaptive DLL scheme provides an efficient and accurate method for real-time MFCV estimation.
• This advancement facilitates continuous muscle fatigue monitoring and opens avenues for new research in myoelectric fatigue.
• The findings suggest potential for deeper insights into the physiological processes underlying muscle fatigue.