D.-S. Huang, L. Heutte, and M. Loog (Eds.): ICIC 2007, LNCS 4681, pp. 1257–1265, 2007. © Springer-Verlag Berlin Heidelberg 2007 Study on Online Gesture sEMG Recognition Zhangyan Zhao 1 , Xiang Chen 1 , Xu Zhang 1 , Jihai Yang 1 , Youqiang Tu 1 , Vuokko Lantz 2 , and Kongqiao Wang 3 1 Department of Electronics Science & Technology University of Science & Technology of China, Hefei, China, 230027 zzylzsd@mail.ustc.edu.cn 2 Nokia Research Center, Interaction CTC, Interacting in Smart Environments P.O. Box 407, FI-00045 Nokia Group, Finland vuokko.lantz@nokia.com 3 Nokia Research Center, NOKIA (CHINA) Investment CO., LTD., Beijing, 100013 Abstract. We have realized an online gesture recognition platform for hand gestures 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. Keywords: surface EMG, online gesture recognition. 1 Introduction At present, there are many alternative methods suitable for the capture and recognition of hand gestures, such as video signal and image analysis techniques, data gloves [1] with position sensors, or surface electromyogram (sEMG) sensors [2] and various pattern recognition algorithms. Recognition approaches which are based image analysis are agile in the sense that they can recognize many different kinds of gestures but they can adapt poorly to the changes in the environment. Gesture recognition with data gloves do not suffer from such adaptability problems and thus provides steadier recognition performance but the manufacturing price is much higher. Surface EMG -based gesture capture is easy to set up and the signals do not depend on the environmental factors. However, the gesture recognition is based on indirect information as the sEMG sensors measure muscle activities and not the movements of the hand and fingers themselves. Therefore, in order to achieve acceptable recognition performance with sEMG sensors, the feature extraction and classification methods need to be designed carefully. A sEMG-based hand gesture recognition system can be used for controlling other devices via a computer interface. There are various applications areas in the realm of HCI: artificial limbs, sport electronic devices, virtual reality games, to name a few. online gesture recognition is a prerequisite for the practical application of sEMG-based