Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA.
Department of Computer Science and Engineering, Dankook University, Yongin-si 16890, Korea.
Sensors (Basel). 2020 Jul 18;20(14):4001. doi: 10.3390/s20144001.
Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.
步态是一种用于识别个体的特征。由于现在可以通过多种类型的设备来获取人类步态信息，因此许多研究已经提出了使用步态信息的生物识别方法。随着研究的不断深入，通过从多模态传感器收集信息，这项技术在识别准确性方面的性能得到了提高。然而，在过去的研究中，步态信息是使用辅助设备收集的，而识别准确性还不足以用于生物识别。在本研究中，我们提出了一种基于深度学习的生物识别模型，通过可穿戴设备（即鞋垫）收集的步态信息来识别个人。当使用多模态传感时，所提出模型的识别准确率超过 99%。
