AGR: Acoustic Gait Recognition Using Interpretable Micro-Range Profile

In recent times, gait recognition, a type of biometric identification, has been widely used for area access control and smart homes. It improves convenience, privacy, and personalized experiences. Contemporary academic inquiry centers on privacy-preserving wireless sensing solutions as substitutes for computer vision. Yet, prevailing strategies heavily lean on abstract features, leading to inherent limitations in interpretability and stability. Fortunately, the widespread utilization of smart speakers has opened up opportunities for acoustic sensing, making it possible to extract more interpretable features. In this paper, we further push the limit of acoustic recognition with visual interpretability by sequentially visualizing fine-grained acoustic human gait features. The construction of initial gait profiles involves matrixing and compressing multipath gait echoes, resulting in imperceptible gait indications. Interpretability is then achieved through novel micro-range profiles, incorporating innovations such as clutter elimination using the Mobile Target Detector (MTD), compensation for farther echo strength, and subtraction of macro torso migration. These interpretable gait profiles offer practical benefits by enhancing data utilization, optimizing abnormal data handling, and improving model stability. Extensive evaluations with an open experimental scenario have been conducted to demonstrate accuracy reaching 97.5% in general, and robust performance against impacts from various practical factors.