Development of a Low-Cost EMG Monitoring and Signal Processing System for Drill Operators

This study presents the development of a low-cost system for real-time monitoring and processing of electromyographic (EMG) signals to study the impact of mechanical vibrations on drill operators, which are known to lead to musculoskeletal disorders and reduced work efficiency. The proposed system comprises an EMG acquisition unit, a vibration generator, and signal processing algorithms to ensure noise reduction and robust data analysis. Signal processing techniques, including notch filtering and Empirical Mode Decomposition (EMD), were employed to ensure high-fidelity signal analysis. Preliminary testing in controlled environments demonstrated the system’s ability to detect the presence of vibrations when using a drill, not the vibration generator, which suggests that muscle activation arises not merely from exposure to vibrations but from the body's efforts to compensate for such stimuli. Observed results indicate the system can detect real-time vibration exposure and its intensity. A third-party Motor Unit Action Potential (MUAP) estimation algorithm was implemented, which could allow the preventive detection of musculoskeletal disorders. The proposed system holds potential for broader ergonomics, rehabilitation, and sports science applications. By offering a portable, cost-effective solution, it addresses a critical gap in real-time monitoring technologies. Future directions include more rigorous testing in real-world settings, exploring the effects of different vibration frequencies, intensities, and directions with a vibration platform, and exploring the use of Artificial Neural Networks (ANN) to help draw actionable conclusions from patterns in MUAP estimation.