This paper presents an experiment using OPENBCI to collect data of two hand gestures and decoding the signal to distinguish gestures. The signal was extracted with three electrodes on the subiect’s forearm and transf...This paper presents an experiment using OPENBCI to collect data of two hand gestures and decoding the signal to distinguish gestures. The signal was extracted with three electrodes on the subiect’s forearm and transferred in one channel. After utilizing a Butterworth bandpass filter, we chose a novel way to detect gesture action segment. Instead of using moving average algorithm, which is based on the calculation of energy, We developed an algorithm based on the Hilbert transform to find a dynamic threshold and identified the action segment. Four features have been extracted from each activity section, generating feature vectors for classification. During the process of classification, we made a comparison between K-nearest-neighbors (KNN) and support vector machine (SVM), based on a relatively small amount of samples. Most common experiments are based on a large quantity of data to pursue a highly fitted model. But there are certain circumstances where we cannot obtain enough training data, so it makes the exploration of best method to do classification under small sample data imperative. Though KNN is known for its simplicity and practicability, it is a relatively time-consuming method. On the other hand, SVM has a better performance in terms of time requirement and recognition accuracy, due to its application of different Risk Minimization Principle. Experimental results show an average recognition rate for the SVM algorithm that is 1.25% higher than for KNN while SVM is 2.031 s shorter than that KNN.展开更多
文摘This paper presents an experiment using OPENBCI to collect data of two hand gestures and decoding the signal to distinguish gestures. The signal was extracted with three electrodes on the subiect’s forearm and transferred in one channel. After utilizing a Butterworth bandpass filter, we chose a novel way to detect gesture action segment. Instead of using moving average algorithm, which is based on the calculation of energy, We developed an algorithm based on the Hilbert transform to find a dynamic threshold and identified the action segment. Four features have been extracted from each activity section, generating feature vectors for classification. During the process of classification, we made a comparison between K-nearest-neighbors (KNN) and support vector machine (SVM), based on a relatively small amount of samples. Most common experiments are based on a large quantity of data to pursue a highly fitted model. But there are certain circumstances where we cannot obtain enough training data, so it makes the exploration of best method to do classification under small sample data imperative. Though KNN is known for its simplicity and practicability, it is a relatively time-consuming method. On the other hand, SVM has a better performance in terms of time requirement and recognition accuracy, due to its application of different Risk Minimization Principle. Experimental results show an average recognition rate for the SVM algorithm that is 1.25% higher than for KNN while SVM is 2.031 s shorter than that KNN.
