Abstract - The long-term goal of this research is to improve the reliability and accuracy of myoelectric prostheses to reduce abandonment rates and enhance user satisfaction. Millions of US citizens suffer from upper limb loss and turn to prostheses to regain function. Myoelectric prostheses utilize signals from muscles to control movements giving added functionality over their passive counterparts. These devices use electromyography (EMG), which captures electrical activity from muscles, to provide more intuitive control. Despite this added functionality, many people end up abandoning their myoelectric prosthesis, with unreliability being a major factor. This study demonstrates that accounting for muscle fatigue within the control algorithm of the  EMG signal improves the stability and reliability of myoelectric prosthesis control over longer durations of muscle activity. The dual-threshold debounced control algorithm exhibited in this paper outperformed the single-threshold method in all durations and showed improved accuracy, particularly during longer flex durations, by maintaining stable control without signal degradation. The results of this study improve the reliability of EMG-based prosthesis control, potentially leading to enhanced reliability and reduced abandonment rates for myoelectric prostheses. These findings could also impact adjacent fields like robotics, where more stable biological signal control could enhance the precision and adaptability of various assistive devices and systems.