Abstract
To explore the potential of gesture recognition based on the A-mode ultrasound (AUS) interface in human-computer interaction (HCI), according to the characteristics of AUS signal, a novel preprocessing approach is designed, feature extraction is performed by the window analysis method, and four methods, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) for classification. The experimental results show that the single feature with the best results can achieve 91.63% accuracy on KNN. Meanwhile, by feature combination, we can achieve 91.91% accuracy on the KNN classifier, it is 3.60% higher than the highest recognition rate of 88.03% among linear fitting features, called KB features. Further, we learn the integration of soft voting for four classifiers, LDA, KNN, SVM, and ANN, and achieve the highest recognition rate of 92.32% on single features and can achieve 93.09% decoding rate on combined features, which is 4.01% higher than 89.08% among KB features with the soft voting method. The experimental results show that AUS has outstanding performance in gesture decoding.
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Wei, S., Zhang, Y., Pan, J., Liu, H. (2022). A Novel Preprocessing Approach with Soft Voting for Hand Gesture Recognition with A-Mode Ultrasound Sensing. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_34
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