Study on Online Gesture sEMG Recognition
نویسندگان
چکیده
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.
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تاریخ انتشار 2007