With the rapid development of the internet, security of personal information becomes more and more important. Personal identification plays an important role in the information security field. To ensure the security of personal information, varieties of personal identification methods have been proposed. The traditional identification methods, such as Identification tag (ID) or Personal Identification Number (PIN), are vulnerable to hackers. Then, the biometrics which use the unique physiological characteristics of human body to identify personal information come into being. But the widely used biometrics at present, such as human face, iris , fingerprint, and voice, also can be falsified and forged. Thus, special attention has been paid to personal identification based on human physiological signals, such as electroencephalograph (EEG) signals, electrocardiograph (ECG) signals and electromyography (EMG) signals. These signals are living body features, which can be used to achieve aliveness detection and prevent the spoofing attacks. Electromyography (EMG) has attracted people's attention because of its unique advantages, which makes it outstanding among other biological characteristics. Thus, using EMG signal for personal identification has broad applications. However, there are few studies on personal identification based on EMG signal. This paper mainly studies the personal identification models based EMG signal which has characteristics of living body and is easy to collect, including offline identification system and real-time identification system. The latter is advanced for the former. The main contributions in this paper are summarized as follows:
(1) For offline identification system, two EMG-based identification systems, which are one-to-many personal identification system and one-to-one identification system, are proposed in this paper. Both systems are consist of data acquisition module, data preprocessing module, feature extraction module, and personal identification module. For the one-to-many personal identification system, the continuous wavelet transform method is adopted to mine the time-frequency features of EMG signals captured from different subjects and convolutional neural network is used to identify the subjects. The identification accuracy of this method can achieve 99.206%. For the one-to-one personal identification system, the siamese network is adopted to learn a similarity metric from data, achieving the identification accuracy of 99.285%.
(2) Based on the offline identification system, this paper further proposes a real-time identification system based on EMG signal. Different from the offline system, the real-time system includes data acquisition module, data preprocessing module, feature extraction module, personal identification module, and additional gesture detection module. The real-time system focuses on the requirement of response time, so that the system has the characteristics of real-time, high efficiency, and strong robustness. To meet the requirement of real-time, a peak detection algorithm which can detect gesture signals from the signal stream in real time is adopted in the system. Then, to improve the computational efficiency, this paper adopts wavelet transform method combined with one-dimensional convolutional neural networks. Specifically, a discrete wavelet transform method combined with statistical methods is used to process EMG signals and extract features. Then, based on the extracted features, one-dimensional convolutional neural network architecture is delicately designed to identify subjects. The result shows that the identification accuracy for 21 subjects under the hand-open gesture could achieve 98.41% and the processing time between gesture event and identification is 37ms.
|Keyword||身份识别 肌电信号 小波变换 卷积神经网络 峰值检测|
|卢立静. 基于肌电信号的身份识别技术研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
|Files in This Item:|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.