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IMU-based Locomotion Mode Recognition by Deep Convolutional Neural Networks with Wide First-layer Kernels

In this paper, the locomotion mode recognition problem with single inertial measurement unit (IMU) is investigated by developing the novel deep convolutional neural network (CNN) learning models. More precisely, the novel wide first-layer kernels and skip connections are adopted in the CNN model for better feature selection on IMU signals. Based on the measurement data of nine locomotion modes, our experiment results have shown that the employed method can achieve well classification results with high robustness and generalization ability.