Abstract
The main goal of this paper is to design an electromyogram (EMG) pattern classifier which is robust to muscular fatigue effects for human-machine interaction. When a user operates some machines such as a PC or a powered wheelchair using EMG-based interface, muscular fatigue is generated by sustained duration time of muscle contraction. Therefore, recognition rates are degraded by the muscular fatigue. In this paper, an important observation is addressed: the variations of feature values due to muscular fatigue effects are consistent for sustained duration time. From this observation, a robust pattern classifier was designed through the adaptation process of hyperboxes of Fuzzy Min-Max Neural Network. As a result, significantly improved performance is confirmed.
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© 2006 Springer-Verlag Berlin Heidelberg
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Song, JH., Jung, JW., Bien, Z. (2006). Robust EMG Pattern Recognition to Muscular Fatigue Effect for Human-Machine Interaction. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_114
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DOI: https://doi.org/10.1007/11925231_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
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