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Summary. Visions of ambient intelligence and ubiquitous computing involve integrating tiny microelectronic processors and sensors into everyday objects in order to make them "smart." Smart things can explore their environment, communicate with other smart things, and interact with humans, therefore helping users to cope with their tasks in new, intuitive ways. Although many concepts have already been tested out as prototypes in field trials, the repercussions of such extensive integration of computer technology into our everyday lives are difficult to predict. This contribution is a first attempt to classify the social, economic, and ethical implications of this development.

Visions of Pervasive Computing and ambient intelligence involve integrating tiny microelectronic processors and sensors into everyday objects in order to make them "smart." Smart things can explore their environment, communicate with other smart things, and interact with humans, therefore helping users to cope with their tasks in new, intuitive ways. Although many concepts have already been tested out as prototypes in field trials, the repercussions of such extensive integration of computer technology into our everyday lives are difficult to predict. This article is a first attempt to classify the social, economic, and ethical implications of this development.

Gestures can offer an intuitive way to interact with a computer. In this paper, we investigate the question whether gesturing with a mobile phone can help to perform complex tasks involving two devices. We present results from a user study, where we asked participants to spontaneously produce gestures with their phone to trigger a set of different activities. We investigated three conditions (device configurations): phone-to-phone, phone-to-tabletop, and phone to public display. We report on the kinds of gestures we observed as well as on feedback from the participants, and provide an initial assessment of which sensors might facilitate gesture recognition in a phone. The results suggest that phone gestures have the potential to be easily understood by end users and that certain device configurations and activities may be well suited for gesture control.

Abstract

This paper explores how microgestures can allow us to execute a secondary task, for example controlling mobile applications, without interrupting the manual primary task, for instance, driving a car. In order to design microgestures iteratively, we interviewed sports-and physiotherapists while asking them to use task related props, such as a steering wheel, a cash card , and a pen for simulating driving a car, an ATM scenario, and a drawing task. The primary objective here is to define microgestures that are easily performable without interrupting or interfering the primary task. Using expert interviews, we developed a taxonomy that classifies these gestures according to their task context. We also assessed the ergonomic and attentional attributes that influence the feasibility and task suitability of microinteractions, and evaluated their level of resources required. Accordingly, we defined 21 microgestures that allow performing microinteractions within a manual, dual task context. Our taxonomy poses a basis for designing microinteraction techniques.

When camera phones are used as magic lenses in handheld augmented reality applications involving wall maps or posters, pointing can be divided into two phases: (1) an initial coarse physical pointing phase, in which the target can be directly observed on the background surface, and (2) a fine-control virtual pointing phase, in which the target can only be observed through the device display. In two studies, we show that performance cannot be adequately modeled with standard Fitts' law, but can be adequately modeled with a two-component modification. We chart the performance space and analyze users' target acquisition strategies in varying conditions. Moreover, we show that the standard Fitts' law model does hold for dynamic peephole pointing where there is no guiding background surface and hence the physical pointing component of the extended model is not needed. Finally, implications for the design of magic lens interfaces are considered.

We conducted a series of user studies to understand and clarify the fundamental characteristics of pressure in user interfaces for mobile devices. We seek to provide insight to clarify a longstanding discussion on mapping functions for pressure input. Previous literature is conflicted about the correct transfer function to optimize user performance. Our study results suggest that the discrepancy can be explained by different signal conditioning circuitry and with improved signal conditioning the user-performed precision relationship is linear. We also explore the effects of hand pose when applying pressure to a mobile device from the front, the back, or simultaneously from both sides in a pinching movement. Our results indicate that grasping type input outperforms single-sided input and is competitive with pressure input against solid surfaces. Finally we provide an initial exploration of non-visual multimodal feedback, motivated by the desire for eyes-free use of mobile devices. The findings suggest that non-visual pressure input can be executed without degradation in selection time but suffers from accuracy problems.