School of Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain.
Department of Telematic Engineering and UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain.
Sensors (Basel). 2020 Dec 3;20(23):6900. doi: 10.3390/s20236900.
This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone's accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.
本文提出了一种基于用户行走时产生的加速度模式进行生物识别的方法。该方法使用智能手机的加速度计和陀螺仪传感器捕获用户进行步态活动时的数据，并优化了递归神经网络 (RNN) 的设计，以最佳地学习能够更好地描述每个人的特征。该数据库由 15 名用户组成，提供的加速度数据在 X-Y-Z 轴上具有三轴格式。数据经过预处理以估计垂直加速度（在重力方向上）。使用由 LSTM 单元组成的深度递归神经网络模型，分为几个层和密集输出层，用于用户识别。最终架构获得的精度结果在大多数执行中都超过 97%。所提出的基于深度神经网络的架构在不同场景下进行测试，以检查其效率和鲁棒性。
