Abstract—Current commercial Electromyography (EMG) systems struggle with four main drawbacks: limited mobility, high prices, time-sensitive processing demands, and their functional suitability for a wide range of clinical and research environments. This study will solve these issues through the development of an affordable compact EMG system that ensures real-time processing stability. The system design unites surface electrodes with a low-noise amplifier and a microcontroller having performance-enhanced processing ability. The design requirements specify a signal spectrum from 0 to 250 Hz and precise resolution because this combination leads to the correct measurement of muscular activities. The device performed tests through a series of experiments with healthy participants to measure signal accuracy, noise levels, and usability conditions in controlled and semi-controlled testing environments. The results showed a mean signal-to-noise ratio of 28.5 dB with accuracy levels equivalent to commercial systems, which proved that the device provides clean EMG signals across diverse conditions. Results of noise power analysis showed 0.020 μV² in the resting state, but decreased to 0.012 μV² when muscles reached their peak activation strength since effective noise reduction measures were implemented. Despite its advantages, including cost-effective operation along with portability, the system presents restrictions in usage with clinical patients who need additional validation of its effectiveness. The portable EMG device presents a versatile, low-cost apparatus for rehabilitation services, sports science testing, and healthcare monitoring needs and requires continuing development for practical implementation. 

Keywords—electromyography, sensor, microcontroller, amplifier, electrodes

Cite: Salim Fattah Awad, Ahmed Jumaa Lafta, Aya Falah Mahmood, and Fahad Mohanad Kadhim, "Design, Programming, and Evaluation of an Economic Portable Muscle Activity Measurement Device," International Journal of Mechanical Engineering and Robotics Research, Vol. 14, No. 6, pp. 667-675, 2025. doi: 10.18178/ijmerr.14.6.667-675

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