Simulation study of deep-learning-based gait classification of young/elderly adults using Doppler radar

• Main Authors: Toshiyuki Hoshiga, Kenshi Saho, Keitaro Shioiri, Masahiro Fujimoto, Yoshiyuki Kobayashi
• Format: Article
• Language: English
• Published: Elsevier 2021-12-01
• Series: Measurement: Sensors
• Subjects: Gait classification; Micro-Doppler; Deep learning; Convolutional neural network; Spectrogram
• Online Access: http://www.sciencedirect.com/science/article/pii/S2665917421000660

Deep-learning-based gait classification of young and elderly adults using micro-Doppler radar (MDR) is presented in this paper. The MDR signal data were accurately simulated using an open motion-capture gait dataset, and deep-learning classification of the time-velocity distribution (i.e., spectrogram) images calculated with the generated data are presented. Utilizing a simulation, we also investigated the body parts deemed most efficient for classification based on their generation of good MDR data. As a result, the classification rate using whole-body data was 74%. However, this classification rate of using only leg data showed an accuracy of 91%, which indicates that the thighs and shanks are efficient target body parts for the gait classification of both young and elderly adults.