Deep Learning Based on 1-D Ensemble Networks Using ECG for Real-Time User Recognition

The postmobile era will go beyond using individual smart devices and allow for user interaction by connecting various devices with sensing capabilities, such as smartphones, wearable devices, automobiles, and the Internet of Things. Wearable devices can continuously collect a variety of information on the users and their environment as the devices are worn in daily life. Because of this, real-time big data analysis technology is needed. This paper proposes a deep learning-based ensemble network model for improving the performance and overcoming the problems, which can occur on a single network. This model is designed so that the features produced by n number of single networks are combined and relearned. In addition, different parameter values are used on each single network, and the data used in the experiments are generated by the fiducial point method, which uses feature point detection, and the nonfiducial point method for periods of 1 sec and n sec. In the experiment results, in the case of fiducial point-based ECG signals, the ensemble network recognition performance shows a maximum of 0.8% higher accuracy than that of the single network. In the case of a 1 sec period nonfiducial point-based ECG signal, the ensemble network recognition performance is a minimum of 0.4% and a maximum of 1% higher than that of the single network. In the case of an n sec period, there is a maximum difference of 1.3% and the proposed ensemble network shows better performance than the single network.