Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks

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.