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For using surface electromyography (sEMG) in various applications, the   process consists of three parts: an onset time detection for detecting the   first point of movement signals, a feature extraction for extracting the   signal attribution, and a feature classification for classifying the sEMG   signals. The first and the most significant part that influences the   accuracy of other parts is the onset time detection, particularly for   automatic systems. In this paper, an automatic and simple algorithm for the   real-time onset time detection is presented. There are two main processes in   the proposed algorithm; a smoothing process for reducing the noise of the   measured sEMG signals and an automatic threshold calculation process for   determining the onset time. The results from the algorithm analysis   demonstrate the performance of the proposed algorithm to detect the sEMG   onset time in various smoothing-threshold equations. Our findings reveal   that using a simple square integral (SSI) as the smoothing-threshold   equation with the given sEMG signals gives the best performance for the   onset time detection. Additionally, our proposed algorithm is also   implemented on a real hardware platform, namely NI myRIO. Using the   real-time simulated sEMG data, the experimental results guarantee that the   proposed algorithm can properly detect the onset time in the real-time   manner

Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO