Tools supporting immersive live video conferencing (VC) have gained popularity recently across diverse application domains. A core component of the experience is augmenting video communication with multimodal interactive media. While many direct-manipulation techniques for VC communication have been proposed in existing literature, the usability and preferences for these techniques have never been formally studied. In this paper, we examine how embodied interaction democratizes content authoring, and propose a rehearsal-to-performance (RtP) framework along with a VC system, Clio, that enables performers to directly interact with their media using voice, gesture, and external devices such as tablets. We evaluate existing operation-to-modality mappings for VC communication, as well as describe novel mappings not present in the literature. A series of studies demonstrate modality preferences and potentials for incorporating real-time direct-manipulation tools to create expressive augmented VC performances.

The COVID-19 pandemic forced many people to convert their daily work lives to a “virtual” format where everyone connected remotely from their home. In this new, virtual environment, accessibility barriers changed, in some respects for the better (e.g., more flexibility) and in other aspects, for the worse (e.g., problems including American Sign Language interpreters over video calls). Microsoft Research held its first cohort of all virtual interns in 2020. We the authors, full time and intern members and affiliates of the Ability Team, a research team focused on accessibility, reflect on our virtual work experiences as a team consisting of members with a variety of abilities, positions, and seniority during the summer intern season. Through our autoethnographic method, we provide a nuanced view into the experiences of a mixed-ability, virtual team, and how the virtual setting affected the team’s accessibility. We then reflect on these experiences, noting the successful strategies we used to promote access and the areas in which we could have further improved access. Finally, we present guidelines for future virtual mixed-ability teams looking to improve access.

Co-creative AI tools provide a method of creative collaboration between a user and machine. One form of co-creative AI called generative design requires the user to input design parameters and wait substantial periods of time while the system computes design solutions. We explore this interaction dynamic by providing an embodied experience in VR. Calliope is a virtual reality (VR) system that enables users to explore and manipulate generative design solutions in real time. Calliope accounts for the typical idle times in the generative design process by using a virtual environment to encourage parallelized and embodied data-exploration and synthesis, while maintaining a tight human-in-the-loop collaboration with the underlying algorithms. In this paper we discuss design considerations informed by formative studies with generative designers and artists and provide design guidelines to aid others in the development of co-creative AI systems in virtual environments.

We present a novel haptic and audio feedback device that allows blind and visually impaired (BVI) users to understand circuit diagrams. TangibleCircuits allows users to interact with a 3D printed tangible model of a circuit which provides audio tutorial directions while being touched. Our system comprises an automated parsing algorithm which extracts 3D printable models as well as an audio interfaces from a Fritzing diagram. To better understand the requirements of designing technology to assist BVI users in learning hardware computing, we conducted a series of formative inquiries into the accessibility limitations of current circuit tutorial technologies. In addition, we derived insights and design considerations gleaned from conducting a formal comparative user study to understand the effectiveness of TangibleCircuits as a tutorial system. We found that BVI users were better able to understand the geometric, spatial and structural circuit information using TangibleCircuits, as well as enjoyed learning with our tool.

Instructors of hardware computing face many challenges including maintaining awareness of student progress, allocating their time adequately between lecturing and helping individual students, and keeping students engaged even while debugging problems. Based on formative interviews with 5 electronics instructors, we found that many circuit style behaviors could help novice users prevent or efficiently debug common problems. Drawing inspiration from the software engineering practice of coding style, these circuit style behaviors consist of best-practices and guidelines for implementing circuit prototypes that do not interfere with the functionality of the circuit, but help a circuit be more readable, less error-prone, and easier to debug. To examine if these circuit style behaviors could be peripherally enforced, aid an in-person instructor's ability to facilitate a workshop, and not monopolize instructor's attention, we developed CircuitStyle, a teaching aid for in-person hardware computing workshops. To evaluate the effectiveness of our tool, we deployed our system in an in-person maker-space workshop. The instructor appreciated CircuitStyle's ability to provide a broad understanding of the workshop's progress and the potential for our system to help instructors of various backgrounds better engage and understand the needs of their classroom.

We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience.