Jung, Dawoon Nguyen, Mau Dung Park, Mina Kim, Jinwook Mun, Kyung-Ryoul 2024-01-19T18:00:25Z 2024-01-19T18:00:25Z 2021-09-05 2020-04 1534-4320 https://pubs.kist.re.kr/handle/201004/118783 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. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC DISORDERS PARAMETERS SYSTEM Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks Article 10.1109/TNSRE.2020.2977049 1 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.28, no.4, pp.997 - 1005 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 28 4 997 1005 scie scopus 000527793800025 2-s2.0-85083249253 Engineering, Biomedical Rehabilitation Engineering Rehabilitation Article DISORDERS PARAMETERS SYSTEM Foot Spectrogram Legged locomotion Continuous wavelet transforms Acceleration Angular velocity Gait classification deep convolutional neural network spectrogram short-time Fourier transform continuous wavelet transform