Field Programmable Gate Array-Embedded Platform for Dynamic Muscle Fiber Conduction Velocity Monitoring

Area of Science:

• Biomedical Engineering
• Neuroscience
• Sports Science

Background:

• Muscle Fiber Conduction Velocity (MFCV) is a key indicator of muscle health and function.
• Accurate and dynamic MFCV monitoring is crucial for diagnosing neuromuscular disorders and optimizing training.
• Existing methods for MFCV assessment can be cumbersome and lack real-time capabilities.

Purpose of the Study:

• To develop and validate a novel wearable Field Programmable Gate Array (FPGA)-based platform for dynamic MFCV monitoring.
• To assess the system's performance in real-time during gait analysis and fatigue monitoring.

Main Methods:

• A wearable platform utilizing surface electromyography (EMG) sensors and footswitches was designed.
• An iterative algorithm processed digitized EMG signals for MFCV calculation on an FPGA board.
• Data was transmitted wirelessly via a Bluetooth module, ensuring real-time processing within 63.5 ms.

Main Results:

• The system achieved real-time MFCV estimation, crucial for dynamic monitoring.
• During gait analysis, healthy subjects exhibited an average MFCV of 7.6 m/s ± 0.36 m/s.
• In fatigue tests, MFCV decreased significantly from 8.51 m/s (rested) to 4.60 m/s (tired), aligning with established trends.

Conclusions:

• The proposed FPGA-based platform provides a novel, accurate, and wearable solution for dynamic MFCV monitoring.
• The system demonstrates efficacy in both gait analysis and fatigue assessment, offering potential for clinical and sports applications.
• This technology enables non-invasive, real-time evaluation of muscle fiber electrophysiology.