Channel Attention Network for Feature Fusion of Ultrasonic and IMU Signals in Cross-Subject Gait Recognition

In traditional human-machine interface research, a single sensing mode is often adopted, focusing on individual human motion classification experiments. However, the performance often declines when applied across individuals. Ultrasonic and inertial sensing, as mainstream technologies in human-machine interfaces, can respectively detect the muscle states and limb information of the human body, offering potential complementary benefits. The fusion of ultrasonic(US) and Inertial Measurement Unit(IMU) is expected to significantly enhance the generalization ability of physiological signals in motion intention recognition. This paper presents a method that employs fusion of ultrasound and IMU signals, and designs a Convolutional Neural Network with Channel Attention Module (IU-CAM-CNN) for gait classification experiments. The experimental results demonstrate that by introducing channel attention modules, the model's ability to extract features from fused signals can be effectively enhanced, resulting in a classification accuracy of 90% for cross-individual movements. In contrast, traditional LDA and CNN networks achieve only 22% and 47% classification accuracy, respectively. This method provides an effective solution for cross-individual movement intention recognition.