We report on the design of ThinkActive - a system to encourage primary aged school children to reflect on their own personal activity data in the classroom. We deployed the system with a cohort of 30 school children, over a six-week period, in partnership with an English Premier League Football club's health and nutrition programme. The system utilizes inexpensive activity trackers and pseudonymous avatars to promote reflection with personal data using an in-situ display within the classroom. Our design explores pseudonymity as an approach to managing privacy and personal data within a public setting. We report on the motivations, challenges, and opportunities for students, teachers, and third-party providers to engage in the collection and sharing of activity data with primary school children.

Rich metadata is becoming a key part of the broadcast production pipeline. This information can be used to deliver compelling new consumption experiences which are personalized, location-aware, interactive and multi-screen. However, media producers are struggling to generate the metadata required for such experiences, using inefficient post-production solutions which are limited in how much of the original context they can capture. In response, we present Media of Things (MoT), a tool for on-location media productions. MoT enables practical and flexible generation of sensor based point-of-capture metadata. We demonstrate how embedded ubiquitous sensing technologies such as the Internet of Things can be leveraged to produce context rich, time sequenced metadata in a production studio. We reflect on how this workflow can be integrated within the constraints of broadcast production and the possibilities that emerge from access to rich data at the beginning of the production lifecycle to produce well described media for reconfigurable consumption.

Mass adoption and innovation in the field of the Internet of Things has transformed the environments we live in, from stale siloes of technologies into rich interactive playgrounds. Nevertheless, the vast majority of surface area in these spaces are being overlooked and under-utilized in today's research. Surface imaging provides the means to extend and include typically out-of-reach, disconnected objects into these playgrounds. However, existing surface imaging technologies are impractical to embed in everyday environments, restricting researchers from exploring the design and interaction opportunities they can afforded these spaces. In this paper, we propose IRIS, a modular surface imaging prototype capable of providing scalable, low-cost, high-resolution surface imaging. We describe a real-world case study where IRIS is used to identify and track fresh fruit produce being prepared -- a task that is typical infeasible with existing technologies. Through IRIS, we hope to enable the community to exploit these under-explored surface areas and enhance the rich, interactive, connected environments we inhabit.

Live streaming services are a growing form of social media. Most live streaming platforms allow viewers to communicate with each other and the broadcaster via a text chat. However, interaction in a text chat does not work well with too many users. Existing techniques to make text chat work with a larger number of participants often limit who can participate or how much users can participate. In this paper, we describe a new design for a text chat system that allows more people to participate without overwhelming users with too many messages. Our design strategically limits the number of messages a user sees based on the concept of neighborhoods, and emphasizes important messages through upvoting. We present a study comparing our system to a chat system similar to those found in commercial streaming services. Results of the study indicate that the Conversational Circle system is easier to understand and interact with, while supporting community among viewers and highlighting important content for the streamer.

Expressiveness, which we define as the extent to which rich and complex intent can be conveyed through action, is a vital aspect of many human interactions. For instance, paint on canvas is said to be an expressive medium, because it affords the artist the ability to convey multifaceted emotional intent through intricate manipulations of a brush. To date, touch devices have failed to offer users a level of expressiveness in their interactions that rivals that experienced by the painter and those completing other skilled physical tasks. We investigate how data about hand movement -- provided by a motion sensor, similar to those found in many smart watches or fitness trackers -- can be used to expand the expressiveness of touch interactions. We begin by introducing a conceptual model that formalizes a design space of possible expressive touch interactions. We then describe and evaluate Expressy, an approach that uses a wrist-worn inertial measurement unit to detect and classify qualities of touch interaction that extend beyond those offered by today's typical sensing hardware. We conclude by describing a number of sample applications, which demonstrate the enhanced, expressive interaction capabilities made possible by Expressy.