@inproceedings{4745d7b4afeb452a8332eb03fb7a7b33,
title = "A hybrid Cnn-Svm classifier for hand gesture recognition with surface Emg signals",
abstract = "A synthetic approach was proposed to improve the recognition accuracy. Different with the traditional feature extractors, this study used a convolutional neural network (CNN) to automatically extract characteristics from the input of raw EMG image. Then, a Support Vector Machine (SVM) classifier was employed to identify the hand motions. Our experiments showed that the proposed method achieved the accuracy around 2.5% higher than the use of CNN only, and about 9.7% higher than the use of traditional method (i.e. the use of time domain feature and a SVM classifier). Both inter-subject and inter-session tests demonstrated the robustness of the CNN-based feature.",
keywords = "CNN, Features, Hand motion, SVM, Surface EMG",
author = "Hongfeng Chen and Runze Tong and Minjie Chen and Yinfeng Fang and Honghai Liu",
year = "2018",
month = nov,
day = "12",
doi = "10.1109/ICMLC.2018.8526976",
language = "English",
isbn = "978-1-5386-5215-2",
series = "IEEE ICMLC Proceedings Series",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "619--624",
booktitle = "2018 International Conference on Machine Learning and Cybernetics",
address = "United States",
note = "2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018 ; Conference date: 15-07-2018 Through 18-07-2018",
}