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

机译：使用ECG的基于一维集成网络的深度学习进行实时用户识别

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摘要

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.
机译：后移动时代将超越使用单个智能设备的范围，并通过将具有感应功能的各种设备（例如智能手机，可穿戴设备，汽车和物联网）连接起来，实现用户交互。随着设备在日常生活中的佩戴，可穿戴设备可以连续收集有关用户及其环境的各种信息。因此，需要实时大数据分析技术。本文提出了一种基于深度学习的集成网络模型，以提高性能并克服可能在单个网络上发生的问题。设计该模型时，要组合并重新学习由n个单个网络产生的功能。另外，在每个单个网络上使用不同的参数值，并且实验中使用的数据通过基准点方法（使用特征点检测）和非基准点方法生成，时间分别为1秒和n秒。在实验结果中，在基于基准点的ECG信号的情况下，集成网络识别性能显示出最高准确度，比单个网络高0.8％。在基于非基准点的1秒钟周期ECG信号的情况下，集成网络识别性能比单个网络的最低​​识别性能高0.4％，最高可提高1％。在n sec的情况下，最大差异为1.3％，并且所提出的集成网络显示出比单个网络更好的性能。

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