Toolkit research plays an important role in the field of HCI, as it can heavily influence both the design and implementation of interactive systems. For publication, the HCI community typically expects toolkit research to include an evaluation component. The problem is that toolkit evaluation is challenging, as it is often unclear what 'evaluating' a toolkit means and what methods are appropriate. To address this problem, we analyzed 68 published toolkit papers. From our analysis, we provide an overview of, reflection on, and discussion of evaluation methods for toolkit contributions. We identify and discuss the value of four toolkit evaluation strategies, including the associated techniques that each employs. We offer a categorization of evaluation strategies for toolkit researchers, along with a discussion of the value, potential limitations, and trade-offs associated with each strategy.

Recent work in multi-touch tabletop interaction introduced many novel techniques that let people manipulate digital content through touch. Yet most only detect touch blobs. This ignores richer interactions that would be possible if we could identify (1) which hand, (2) which part of the hand, (3) which side of the hand, and (4) which person is actually touching the surface. Fiduciary-tagged gloves were previously introduced as a simple but reliable technique for providing this information. The problem is that its lowlevel programming model hinders the way developers could rapidly explore new kinds of user-and handpartaware interactions. We contribute the TOUCHID toolkit to solve this problem. It allows rapid prototyping of expressive multi-touch interactions that exploit the aforementioned characteristics of touch input. TOUCHID provides an easy-to-use event-driven API. It also provides higher-level tools that facilitate development: a glove configurator to rapidly associate particular glove parts to handparts; and a posture configurator and gesture configurator for registering new hand postures and gestures for the toolkit to recognize. We illustrate TOUCHID's expressiveness by showing how we developed a suite of techniques (which we consider a secondary contribution) that exploits knowledge of which handpart is touching the surface.

Displays are growing in size, and are increasingly deployed in semi-public and public areas. When people use these public displays to pursue personal work, they expose their activities and sensitive data to passers-by. In most cases, such shoulder-surfing by others is likely voyeuristic vs. a deliberate attempt to steal information. Even so, safeguards are needed. Our goal is to mitigate shoulder-surfing problems in such settings. Our method leverages notions of territoriality and proxemics, where we sense and take action based on the spatial relationships between the passerby, the user of the display, and the display itself. First, we provide participants with awareness of shoulder-surfing moments, which in turn helps both parties regulate their behaviours and mediate further social interactions. Second, we provide methods that protect information when shoulder-surfing is detected. Here, users can move or hide information through easy to perform explicit actions. Alternately, the system itself can mask information from the passerby's view when it detects shoulder-surfing moments.

Modern mobile devices allow a rich set of multi-finger interactions that combine modes into a single fluid act, for example, one finger for panning blending into a two-finger pinch gesture for zooming. Such gestures require the use of both hands: one holding the device while the other is interacting. While on the go, however, only one hand may be available to both hold the device and interact with it. This mostly limits interaction to a single-touch (i.e., the thumb), forcing users to switch between input modes explicitly. In this paper, we contribute the Fat Thumb interaction technique, which uses the thumb's contact size as a form of simulated pressure. This adds a degree of freedom, which can be used, for example, to integrate panning and zooming into a single interaction. Contact size determines the mode (i.e., panning with a small size, zooming with a large one), while thumb movement performs the selected mode. We discuss nuances of the Fat Thumb based on the thumb's limited operational range and motor skills when that hand holds the device. We compared Fat Thumb to three alternative techniques, where people had to precisely pan and zoom to a predefined region on a map and found that the Fat Thumb technique compared well to existing techniques.

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