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Access control method using electromyogram signal for consumer electronics system security

As the Internet of Things (IoT) technology develops, Consumer Electronics (CE) is equipped with network function, providing convenience to users. However, since they are connected to each other by network, hacking allows access to a variety of devices, and existing user recognition methods used to control the CE information has vulnerable security. In this paper, we proposed a CE access control method using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based Electromyogram (EMG) signal. EMG signal is preprocessed with a digital filter and then converted into a spectrogram, and the user is recognized using the designed CNN-LSTM. To verify the possibility of proposed CE access control method, the experiment was performed using Ninapro DB2. As a result of the experiment, twenty users showed 99.2% accuracy. In addition, experiment result showing 1.1% higher performance compared to the existing user recognition method using CNN. Therefore, possibility of CE access control using EMG signal of user was confirmed.