Lee Sung-Sin, Choi Sang Tae, Choi Sang-Il
Department of Data Science, Dankook University, Yongin 16890, Korea.
Department of Internal Medicine, Chung-Ang University, Seoul 06984, Korea.
Sensors (Basel). 2019 Apr 12;19(8):1757. doi: 10.3390/s19081757.
In this paper, we proposed a gait type classification method based on deep learning using a smart insole with various sensor arrays. We measured gait data using a pressure sensor array, an acceleration sensor array, and a gyro sensor array built into a smart insole. Features of gait pattern were then extracted using a deep convolution neural network (DCNN). In order to accomplish this, measurement data of continuous gait cycle were divided into unit steps. Pre-processing of data were then performed to remove noise followed by data normalization. A feature map was then extracted by constructing an independent DCNN for data obtained from each sensor array. Each of the feature maps was then combined to form a fully connected network for gait type classification. Experimental results for seven types of gait (walking, fast walking, running, stair climbing, stair descending, hill climbing, and hill descending) showed that the proposed method provided a high classification rate of more than 90%.
在本文中，我们提出了一种基于深度学习的步态类型分类方法，该方法使用了带有各种传感器阵列的智能鞋垫。我们使用内置在智能鞋垫中的压力传感器阵列、加速度传感器阵列和陀螺仪传感器阵列来测量步态数据。然后使用深度卷积神经网络（DCNN）提取步态模式的特征。为了实现这一点，将连续步态周期的测量数据划分为单位步长。接着对数据进行预处理以去除噪声，随后进行数据归一化。然后通过为从每个传感器阵列获得的数据构建独立的DCNN来提取特征图。然后将每个特征图组合起来形成一个用于步态类型分类的全连接网络。对七种步态（行走、快走、跑步、爬楼梯、下楼梯、爬山和下山）的实验结果表明，所提出的方法提供了超过90%的高分类率。
