Human gait has served as a useful barometer of health. Existing studies for automatic categorization of gait have been limited to a binary classification of pathological and non-pathological gait and provided low accuracy in multi-classification. This study aimed to propose a novel approach that can multi-classify gait with no visually discernible difference in characteristics. Sixty-nine participants without gait disturbance were recruited. Twenty-nine of the participants were semi-professional athletes, and 19 were ordinary people. The remaining 21 participants were people with subtle foot deformities. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while walking. The gait spectrograms were acquired by applying time-frequency analyses to the lower body movement signals measured in one stride and used to train the deep convolutional neural network-based classifiers. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The foot, shank, and thigh spectrograms enabled complete classification of the three groups even without requiring a sophisticated process for feature engineering. This is the first study that employed the spectrographic approach in gait classification and achieved reliable multi-classification of gait without observable differences in characteristics using the deep convolutional neural networks.

中文翻译：

---

使用时频表示和深度卷积神经网络对步态进行多分类

人的步态已经成为健康的晴雨表。现有的关于步态自动分类的研究仅限于病理和非病理步态的二元分类，并且在多分类中准确性较低。这项研究旨在提出一种新颖的方法，可以对步态进行多分类，而没有视觉上可辨别的特征差异。招募了69名没有步态障碍的参与者。参与者中有29名是半职业运动员，而19名是普通人。其余的21名参与者是脚部轻微变形的人。使用在行走时连接到脚，小腿，大腿和后骨盆的惯性测量单元测量3轴加速度和3轴角速度信号。步态频谱图是通过对一步测量的下半身运动信号进行时频分析而获得的，并用于训练基于深度卷积神经网络的分类器。对80％的样本进行了四重交叉验证，其余20％用作测试数据。足部，足部和大腿的频谱图即使没有复杂的特征工程流程也可以对这三类进行完整的分类。这是第一项在步态分类中采用光谱方法并实现了可靠的步态多分类的研究，该研究使用深度卷积神经网络在特征上未观察到差异。