Gesture recognition with wearable 9-axis sensors

Abstract

The hand gesture recognition is applied in many kinds of technology such as mobile phone applications, wearable wireless devices, sports detection, or video game. In this paper, we recorded signals of eight kinds of hand movements into computer using wearable wireless device with nine-axis sensor (including accelerometer, gyroscope and magnetometer sensors) worn on the wrist, then recognized gestures with machine learning classification process. In order to achieve higher recognition accuracy, we apply feature extraction to get well-distinguishable features. We used principal component analysis (PCA) and linear discriminant analysis (LDA) to extract features. The advantages of PCA and LDA are reducing dimensions of data while preserving as much of the class discriminatory information as possible and reducing the training time of classification. Last, with the support vector machine (SVM), we can recognize movement with less computation time even with higher accuracy, and it also supports data with high dimension. We can model even non-linear relations with more precise classification due to SVM kernels. In our experiment, we can get the accuracy of recognition at 99.63% for 8 classes with 20 subjects data for 5 times each in user-dependent case, and 12 subjects testing data for user-independent case with recognition rate at 88.43%.