Apidet Booranawong

Booncharoen Wongkittisuksa

Chaiwat Lersviriyanantakul

Kiattisak Sengchuai

Nattha Jindapetch

Pornchai Phukpattaranont

Thermal Science

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.</jats:p

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

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