Billinghurst, MarkPai, Yun SuenGunasekaran, Tamil Selvan2022-07-142022-07-142022https://hdl.handle.net/2292/60415Body-Centric interaction allows people to perform operations outside the device’s screen without working within a small viewport or input area. Previous works exploring body-centric interaction have been implemented using commercial sensors such as cameras, depth sensors, Inertial Measurement Units (IMUs), and so on. However, these have privacy issues and perform poorly under noise such as occlusion, illumination and hand movements. To that end, this thesis explores how radar sensing can support body-centric interaction. The first part of the thesis introduces RadarHand, an on-body interface using a wearable radarbased system on the wrist for proprioceptive input gestures. I introduce the gesture design based on hand topography, where I conducted a proprioceptive gesture analysis. I then grouped and trained these gestures using deep learning models to establish which gesture combinations are most suitable for our use cases. Finally, I evaluated a real-time model and reported its performance and shortcomings. Next, I introduce RadarDesk, an around-body interface using radar-based identification (ID) for tangible interactions. RadarDesk can track and identify objects on a tabletop scale. I classify different objects embedded with low-cost radar reflectors on a tabletop setup. I also introduce Stackable IDs, where each objects can be stacked to produce unique IDs. The result allows RadarDesk to accurately identify visually identical objects embedded with different low-cost reflector configurations. When combined with a radar’s ability for tracking, it creates novel around-body interaction modalities.Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated.https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htmhttp://creativecommons.org/licenses/by-nc-sa/3.0/nz/Thesis2022-06-22Copyright: the authorhttp://purl.org/eprint/accessRights/OpenAccess