Smart devices, such as smartphones, smartwatches, and smart speakers, become increasingly popular and change people's daily life in a profound way. However, the usability and functionality of these devices are still limited since there lack of effective sensing techniques to understand users and environments. Particularly, the motion and shape of a target, such as a user's hand or a nearby object, are the most important information to understand user behaviors or gain knowledge about

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... s, but the existing sensors on smart devices cannot capture the fine-grained motion of a target or the shape of an object under darkness or occlusion. To solve this problem, we study novel sensing techniques using acoustic signals. Specifically, we use speakers to play inaudible signals. During the propagation, the signals will be affected by the motion and shape of a target object in a deterministic way. By collecting and analyzing these signals, we can infer such information about the target object. We explore acoustic signals since they are beneficial to achieve high sensing accuracy due to low propagation speed. They are also widely available since most smart devices are equipped with speakers and microphones. In this dissertation, we develop four acoustic sensing systems on smart devices. We first propose two motion tracking systems. They are called device-based tracking and device-free tracking, respectively, according to whether a user needs to carry a device (e.g., a smartphone or smart watch) for tracking purpose. Based on tracking techniques, we design a novel application that allows a drone to automatically follow a user for video taping. Beside motion tracking, we also design acoustic imaging system to sense the object shape under darkness or occlusion. We elaborate these systems as below. First, we develop a device-based motion tracking system that turns a smartphone into a motion controller. It provides a new way to interact with and control video games, VR/AR headsets, and smart appliances. In this system, w [...]