Technology advancements such as smart glasses offer novel opportunities to interact with technology on-the-go. This study examined use of two types of smart glasses (Everysight Raptor and Vuzix m100) for reading during walking and compared to a mobile phone. Fifty-two healthy adults performed single-task and dual-task walking and reading in one of two environments. Objective measures included gait speed and variability and reading speed and comprehension, and subjective measures included workload and usability. Results indicated that when using smart glasses, participants walked slower and with larger gait variability, read less text and had lower comprehension scores across both environments. Participants perceived the glasses as less usable and more demanding than the phone, with no differences between the two types of glasses. This work provides a benchmark for the negative impact of using smart glasses during walking indoors and outdoors. Potential implications are higher risk for community ambulation, suggesting a need for consideration of human attentional capacity when designing smart glasses interfaces and future urban environments.

Body gestures could be used as an intuitive interaction method with computerized systems. Previous studies explored gesture-based interaction mostly with digital displays, thus there is no standard set of gestures for a system that lacks a display. In this work we conducted a pilot study to explore the potential of using gestures to control an eye tracker based mobile museum visitors’ guide. Our objective was to identify a user-defined set of gestures for controlling the mobile guide. In this work we present the preliminary results of the experiment and discuss the participants’ suggestions and concerns for using this type of interaction.

We present a study aimed to better understand users' perceptions of foot gestures employed on a horizontal surface. We applied a user elicitation methodology, in which participants were asked to suggest foot gestures to actions (referents) in three conditions: standing up in front of a large display, sitting down in front of a desktop display, and standing on a projected surface. Based on majority count and agreement scores, we identified three gesture sets, one for each condition. Each gesture set shows a mapping between a common action and its chosen gesture. As a further contribution, we suggest a new measure called specification score, which indicates the degree to which a gesture is specific, preferable and intuitive to an action in a specific condition of use. Finally, we present measurable insights that can be implemented as guidelines for future development and research of foot interaction.

A promising new way of interacting with computing devices is by using our feet. Foot interaction has the potential of being an intuitive, easy to use and enjoyable way of interaction. However, there are very few guidelines for using foot interaction, or specifically foot gestures. In this research we conduct a user elicitation study for foot interaction on a horizontal surface to produce user-defined gesture sets for actions taken from two domains -- typical GUI actions and avatar controls. We analyze how foot gestures differentiate from hand gestures, point out foot gesture properties and discuss general observations.

We present a novel system for browsing through a very large set of images according to similarity. The images are dynamically placed on a 2D canvas next to their nearest neighbors in a high-dimensional feature space. The layout and choice of images is generated on-the-fly during user interaction, reflecting the user's navigation tendencies and interests. This intuitive solution for image browsing provides a continuous experience of navigating through an infinite 2D grid arranged by similarity. In contrast to common multidimensional embedding methods, our solution does not entail an upfront creation of a full global map. Image map generation is dynamic, fast and scalable, independent of the number of images in the dataset, and seamlessly supports online updates to the dataset. Thus, the technique is a viable solution for massive and constantly varying datasets consisting of millions of images. Evaluation of our approach shows that when using DynamicMaps, users viewed many more images per minute compared to a standard relevance feedback interface, suggesting that it supports more fluid and natural interaction that enables easier and faster movement in the image space. Most users preferred DynamicMaps, indicating it is more exploratory, better supports serendipitous browsing and more fun to use