Keywords
Cameras are a useful source of input for many interactive applications, but computer vision programming is difficult and requires specialized knowledge that is out of reach for many HCI practitioners. In an effort to learn what makes a useful computer vision design tool, we created Eyepatch, a tool for designing camera-based interactions, and evaluated the Eyepatch prototype through deployment to students in an HCI course. This paper describes the lessons we learned about making computer vision more accessible, while retaining enough power and flexibility to be useful in a wide variety of interaction scenarios.
Modern brain sensing technologies provide a variety of methods for detecting specific forms of brain activity. In this paper, we present an initial step in exploring how these technologies may be used to perform task classification and applied in a relevant manner to HCI research. We describe two experiments showing successful classification between tasks using a low-cost off-the-shelf electroencephalograph (EEG) system. In the first study, we achieved a mean classification accuracy of 84.0% in subjects performing one of three cognitive tasks - rest, mental arithmetic, and mental rotation - while sitting in a controlled posture. In the second study, conducted in more ecologically valid setting for HCI research, we attained a mean classification accuracy of 92.4% using three tasks that included non-cognitive features: a relaxation task, playing a PC based game without opponents, and engaging opponents within the game. Throughout the paper, we provide lessons learned and discuss how HCI researchers may utilize these technologies in their work.
In this paper, we describe Augur, a groupware calendar system to support personal calendaring practices, informal workplace communication, and the socio-technical evolution of the calendar system within a workgroup. Successful design and deployment of groupware calendar systems have been shown to depend on several converging, interacting perspectives. We describe calendar-based work practices as viewed from these perspectives, and present the Augur system in support of them. Augur allows users to retain the flexibility of personal calendars by anticipating and compensating for inaccurate calendar entries and idiosyncratic event names. We employ predictive user models of event attendance, intelligent processing of calendar text, and discovery of shared events to drive novel calendar visualizations that facilitate interpersonal communication. In addition, we visualize calendar access to support privacy management and long-term evolution of the calendar system.
