IMFi: IMU-WiFi based Cross-modal Gait Recognition System with Hot-Deployment

WiFi-based gait recognition is an appealing device-free user identification method, but the environment-sensitive WiFi signal hinders it from easy deployment for a new environment. On the other hand, the Inertial Measurement Unit (IMU) based method could obtain environment-independent gait features, however, it suffers from uncomfortable experiences due to device wearing. In this paper, we propose IMFi, a novel cross-modal gait recognition system to achieve device-free and easy deployment at the same time. We carefully choose the torso and foot speed curves as common features for cross-modal matching. In the enrollment phase, we extract and store the environment-independent IMU-based gait features with two IMU devices attached to the waist and ankle, respectively. In the recognition phase, we retrieve environment-related CSI-based gait features for user identification, along with the environment adaptive Principal Component Analysis (PCA) selection method for better noise reduction. We perform cross-modal matching between IMU and CSI-based features through a simple Convolution Neural Network (CNN) with a limited number of trained environments. The effectiveness of the proposed system is verified via extensive experiments. The results demonstrate that IMFi could be easily deployed to the new environment without the need for retraining. Specifically, our proposed system achieves 85% binary classification accuracy and 96% top-3 multi-class classification accuracy in the new environment.