Towards Acoustic Sensing in Human-centered Enhanced Interaction

  In recent years, the spread of smart devices has led to big growth and fast improvements in technology. As a result, the interaction between humans and computers has become very important. Researchers have looked into many ways to create connections between smart devices and humans, aiming to make interactions that are focused on the human user and improve comfort and convenience. However, using these methods and systems in real-world settings is still very challenging. This thesis looks at acoustic sensing and aims to create practical systems that can be easily used in daily life.
Firstly, we create a strength-based acoustic tracking system that works well in small spaces. Unlike traditional systems that need localization beacons to be placed far apart for better accuracy, our system lets us place beacons close together, giving us high tracking accuracy.
Secondly, we suggest a way to guess human attention by watching head movement. By keeping an eye on the movement of the earphones, we can accurately follow the motion of the user's head, letting us guess their attention. Then, we deal with the limits set by the Bluetooth Low Energy protocol by using the nonlinearity properties of wireless earphones. This helps us get past the limits of low sampling rates and BLE compression, letting us effectively track earphones within the framework of commercial wireless earphones.
Lastly, we extend the ability of acoustic touch sensing to underwater settings, where normal touch screens don't work at all. This makes it possible for smartphones to take better underwater photos. Through these contributions, we aim to push the field of acoustic sensing forward and develop practical systems that can be used in various real-world settings to improve the interaction between humans and computers.

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