UAO: Unlocking Pedestrian Tracking With Acoustic Devices

This paper propose UAO, an acoustic device-based pedestrian tracking technique that leverages acoustic sensing to detect steps, extract step features and estimate step length, thereby calculating the walking distance. UAO enables handheld devices equipped with commercial speakers and microphones to track pedestrians without the need for calibration to achieve accurate stride length estimation (SLE) on acoustic devices. UAO performs active acoustic sensing, packaging the cross-correlation results of acoustic data with the transmitted signal template into acoustic images. Innovatively, this paper employs an acoustic image target extraction method based on few-shot learning to accurately capture step features and detect steps, further extracting detailed step parameters, including step frequency and the vertical displacement of the body's center of mass, for an adaptive SLE model. We achieve 99.1$%$step recognition accuracy and less than 2$%$SLE error across modes, including Reading, Picturing, Calling, and Swinging. Compared to existing learning-based Inertial Odometry, the proposed UAO requires only 10$%$of the dataset size during the training phase.