Exploring Application Opportunities for Smart Vehicles in the Continuous Interaction Space Inside and Outside the Vehicle

We describe applications that implement interactions between the driver and their smart vehicle in a continuous interaction space characterized by the physical distance to the vehicle and by the smart devices that implement those interactions. Specifically, we demonstrate the principles of smart vehicle proxemics with smart rings, smartwatches, smartphones, and other devices employed to interact with the in-vehicle infotainment system while the driver traverses five distinctly identifiable zones, from inside the vehicle to the personal, proximal, distant, and covert zone outside the vehicle. We present engineering details of our applications that capitalize on standardized web technology (HTML, CSS, JavaScript), communication protocols (WebSocket), and data formats (JSON) and, thus, enable straightforward extension to accommodate other smart devices for new interactions with smart vehicles. We also point to future opportunities for designing interactions from a distance and function of the distance between the driver and their vehicle

Smart Vehicle Proxemics: A Conceptual Framework Operationalizing Proxemics in the Context of Outside-the-Vehicle Interactions

We introduce smart vehicle proxemics, a conceptual framework for interactive vehicular applications that operationalizes proxemics to outside-the-vehicle interactions. We identify four zones around the vehicle affording different kinds of interactions and discuss the corresponding conceptual space along three dimensions (physical distance, interaction paradigm, and goal). We study the dimensions of this framework and synthesize our findings regarding drivers’ preferences for (i) information to obtain from their vehicles at a distance, (ii) system functions of their vehicles to control remotely, and (iii) devices (e.g., smartphones, smartglasses, smart key fobs) for interactions outside the vehicle. We discuss the positioning of smart vehicle proxemics in the context of proxemic interactions more generally, and expand on the dichotomy and complementarity of outside-the-vehicle and inside-the-vehicle interactions for new applications enabled by smart vehicle proxemics

Understanding Gesture Articulations Variability

Part 4: Information on Demand, on the Move, and Gesture InteractionInternational audienceInterfaces based on mid-air gestures often use a one-to-one mapping between gestures and commands, but most remain very basic. Actually, people exhibit inherent intrinsic variations for their gesture articulations because gestures carry dependency with both the person producing them and the specific context, social or cultural, in which they are being produced. We advocate that allowing applications to map many gestures to one command is a key step to give more flexibility, avoid penalizations, and lead to better user interaction experiences. Accordingly, this paper presents our results on mid-air gesture variability. We are mainly concerned with understanding variability in mid-air gesture articulations from a pure user-centric perspective. We describe a comprehensive investigation on how users vary the production of gestures under unconstrained articulation conditions. The conducted user study consisted in two tasks. The first one provides a model of user conception and production of gestures; from this study we also derive an embodied taxonomy of gestures. This taxonomy is used as a basis for the second experiment, in which we perform a fine grain quantitative analysis of gesture articulation variability. Based on these results, we discuss implications for gesture interface designs

FittsFarm: Comparing Children’s Drag-and-Drop Performance Using Finger and Stylus Input on Tablets

We used a two-dimensional Fitts’ law task to compare finger and stylus input with children when performing drag-and-drop tasks on a tablet. Twenty-eight children completed the study. Drag-and-drop performance was significantly better using a low-cost stylus compared to finger input. Throughput was 9% higher for stylus input (2.55bps) compared to finger input (2.34bps). Error rates were 35% percent higher for finger input (12.6%) compared to stylus input (9.3%). Error rates approximately doubled with smaller targets. There was no significant difference observed for movement time between input methods. Findings indicate schools should consider providing children with a low-cost stylus for educational activities on tablets