Analysis of the Effects of EMG Sensor Circuitries On Output Signal Quality

Electromyography (EMG) is a popular technic used for analysis and study of muscle activity. However, noises and artefacts are needed to be removed from the signal in order to make it usable for various applications. Besides, the signal is needed to be sampled and quantized to digitally record it. This paper is aimed to design and optimize the low-cost and high-performance EMG sensor system for biomedical applications. It focuses on some of the most important hardware parameters e.g., filter order, sampling rate, and quantization level in developing an EMG sensor system. In addition, it considers the impact on SNR of output signal and hardware complexity tradeoffs. Analog filtering, sampling, and quantization stages were carried out on a simulated EMG signal, and the effect of these parameters was studied using heatmaps and correlation matrices. It is observed from the results that a high sampling rate, low-order filter, and avoiding aliasing effect reduce the noise in a way that digital filters may be employed for post-processing. This approach minimizes circuit complexity while maintaining high signal quality and furthers the development of accessible EMG sensor designs. The proposed study has presented a robust framework in the design of EMG sensors using cost-efficient components suitable for wide applications in research and engineering.