Pattern-recognition-based myoelectric control systems have been shown to be accurate in controlled laboratory experiments where users are often restricted to perform discrete, segmented and constant force contractions. Such types of contractions are insufficient for real functional use that requires a controller to interpret both constant force contractions and dynamic transitions between motion classes. This explains why pattern recognition based myoelectric control systems have often been unreliable in real-world settings and the dynamic conditions required for functional use. This work develops an original pattern recognition based myoelectric control system that improves system performance during dynamic transitions between motion classes. The proposed architecture uses feature estimate methods inspired by Kalman filters and enables different control strategies depending on whether users perform constant force contractions or dynamic transitions. The system was developed offline and was then evaluated in a real-time (user-in-the-loop) task to verify if it improved usability. For both offline and real-time analyses, the system was compared to state-of-the-art pattern recognition systems based on linear discriminant analysis. The real-time results showed that the proposed system allowed users to perform significantly (p < .05) more tasks in a significantly (p < .05) less amount of time, and the proposed system also obtained a better speed-accuracy tradeoff, suggesting improved usability.Furthermore, the system improved the quality of descriptive features and allowed new users to better learn how to control pattern recognition based myoelectric control system.
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