In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.

Display devices on and around the body such as smartwatches, head-mounted displays or tablets enable users to interact on the go. However, diverging input and output fidelities of these devices can lead to interaction seams that can inhibit efficient mobile interaction, when users employ multiple devices at once. We present MultiFi, an interactive system that combines the strengths of multiple displays and overcomes the seams of mobile interaction with widgets distributed over multiple devices. A comparative user study indicates that combined head-mounted display and smartwatch interfaces can outperform interaction with single wearable devices.

We have recently begun to see hardware support for the tabletop user interface, offering a number of new ways for humans to interact with computers. Tabletops offer great potential for face-to-face social interaction; advances in touch technology and computer graphics provide natural ways to directly manipulate virtual objects, which we can display on the tabletop surface. Such an interface has the potential to benefit a wide range of the population and it is important that we design for usability and learnability with diverse groups of people.This paper describes the design of SharePic -- a multiuser, multi-touch, gestural, collaborative digital photograph sharing application for a tabletop -- and our evaluation with both young adult and elderly user groups. We describe the guidelines we have developed for the design of tabletop interfaces for a range of adult users, including elders, and the user interface we have built based on them. Novel aspects of the interface include a design strongly influenced by the metaphor of physical photographs placed on the table with interaction techniques designed to be easy to learn and easy to remember. In our evaluation, we gave users the final task of creating a digital postcard from a collage of photographs and performed a realistic think-aloud with pairs of novice participants learning together, from a tutorial script.