Typing on mobile devices is a common and complex task. The act of typing itself thereby encodes rich information, such as the typing method, the context it is performed in, and individual traits of the person typing. Researchers are increasingly using a selection or combination of experience sampling and passive sensing methods in real-world settings to examine typing behaviours. However, there is limited understanding of the effects these methods have on measures of input speed, typing behaviours, compliance, perceived trust and privacy. In this paper, we investigate the tradeoffs of everyday data collection methods. We contribute empirical results from a four-week field study (N=26). Here, participants contributed by transcribing, composing, passively having sentences analyzed and reflecting on their contributions. We present a tradeoff analysis of these data collection methods, discuss their impact on text-entry applications, and contribute a flexible research platform for in the wild text-entry studies.

Composite data physicalizations allow for the physical reconfiguration of data points, creating new opportunities for interaction and engagement. However, there is a lack of understanding of people’s strategies and behaviors when directly manipulating physical data objects. In this paper, we systematically characterize different reconfiguration strategies using six exemplar physicalizations. We asked 20 participants to reorganize these exemplars with two levels of restriction: changing a single data object versus changing multiple data objects. Our findings show that there were two main reconfiguration strategies used: changes in proximity and changes in atomic orientation. We further characterize these using concrete examples of participant actions in relation to the structure of the physicalizations. We contribute an overview of reconfiguration strategies, which informs the design of future manually reconfigurable and dynamic composite physicalizations.

Amongst the variety of (multi-modal) interaction techniques that are being developed and explored, the Motion Matching paradigm provides a novel approach to selection and control. In motion matching, users interact by rhythmically moving their bodies to track the continuous movements of different interface targets. This paper builds upon the current algorithmic and usability focused body of work by exploring the product possibilities and implications of motion matching. Through the development and qualitative study of four novel and different real-world motion matching applications --- with 20 participants --- we elaborate on the suitability of motion matching in different multi-user scenarios, the less pertinent use in home environments and the necessity for multi-modal interaction. Based on these learnings, we developed three novel motion matching based interactive lamps, which report on clear paths for further dissemination of the embodied interaction technique's experience. This paper hereby informs the design of future motion matching interfaces and products.

Continuous developments in the field of Human Computer Interaction (HCI) are resulting in an omnipresence of digital technologies in our everyday lives, which is also visible in the presence of supportive technologies in education. These technologies, e.g. tablets and computers, usually require focused attention to be operated, which hinders teachers from appropriating them while teaching. Peripheral interactive systems, which do not require focused attention, could play a role in relieving teachers' cognitive load, such that mental resources are freed to focus on other teaching tasks. This paper presents an exploratory study on enabling such cognitive offloading through peripheral interaction in the classroom. We present the design and a seven-week field deployment of FireFlies2 interactive tangible pixels which are distributed over the classroom. Our findings show that FireFlies2 supported cognitive processes of teachers and pupils in a number of scenarios.

This paper presents a prototype of a smart home control system operated through motion matching input. In motion matching, targets move continuously in a singular and pre-defined path; users interact with these targets by tracking their movement for a short period of time. Our prototype captures user input through the motion sensors embedded in off-the-shelf smartwatches while users track the moving targets with their arms and hands. The wearable nature of the tracking system makes our prototype ideal for interaction with numerous devices in a smart home.

SmoothMoves is an interaction technique for augmented reality (AR) based on smooth pursuits head movements. It works by computing correlations between the movements of on-screen targets and the user's head while tracking those targets. The paper presents three studies. The first suggests that head based input can act as an easier and more affordable surrogate for eye-based input in many smooth pursuits interface designs. A follow-up study grounds the technique in the domain of augmented reality, and captures the error rates and acquisition times on different types of AR devices: head-mounted (2.6%, 1965ms) and hand-held (4.9%, 2089ms). Finally, the paper presents an interactive lighting system prototype that demonstrates the benefits of using smooth pursuits head movements in interaction with AR interfaces. A final qualitative study reports on positive feedback regarding the technique's suitability for this scenario. Together, these results indicate show SmoothMoves is viable, efficient and immediately available for a wide range of wearable devices that feature embedded motion sensing.