Abstract
We have realized an online gesture recognition platform for hand gestores using 2-channel surface EMG signals acquired from the forearm. Several features, such as AMV, AMV ratio and fourth-order AR model coefficients are extracted from the sEMG signal and the gesture segments are recognized with a Weighted Euclidean Distance Classifier. An above 90% recognition rate has been achieved with only a 400 μs recognition time. The methods developed in this study are aimed to be applied in a fast-response sEMG control system and be transplanted into an embedded microprocessor system. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Zhao, Z., Chen, X., Zhang, X., Yang, J., Tu, Y., Lantz, V., & Wang, K. (2007). Study on online gesture sEMG recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4681 LNCS, pp. 1257–1265). Springer Verlag. https://doi.org/10.1007/978-3-540-74171-8_128
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