human

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.

We have previously developed a collaborative infrastructure called SCAPE - an acronym for Stereoscopic Collaboration in Augmented and Projective Environments - that integrates the traditionally separate paradigms of virtual and augmented reality. In this paper, we extend SCAPE by formalizing its underlying mathematical framework and detailing three augmented Widgets constructed via this framework: CoCylinder, Magnifier, and CoCube. These devices promote intuitive ways of selecting, examining, and sharing synthetic objects, and retrieving associated documentary text. Finally we present a testbed application to showcase SCAPE's capabilities for interaction in large, augmented virtual environments.

This paper presents TinyMotion, a pure software approach for detecting a mobile phone user's hand movement in real time by analyzing image sequences captured by the built-in camera. We present the design and implementation of TinyMotion and several interactive applications based on TinyMotion. Through both an informal evaluation and a formal 17-participant user study, we found that 1. TinyMotion can detect camera movement reliably under most background and illumination conditions. 2. Target acquisition tasks based on TinyMotion follow Fitts' law and Fitts law parameters can be used for TinyMotion based pointing performance measurement. 3. The users can use Vision TiltText, a TinyMotion enabled input method, to enter sentences faster than MultiTap with a few minutes of practicing. 4. Using camera phone as a handwriting capture device and performing large vocabulary, multilingual real time handwriting recognition on the cell phone are feasible. 5. TinyMotion based gaming is enjoyable and immediately available for the current generation camera phones. We also report user experiences and problems with TinyMotion based interaction as resources for future design and development of mobile interfaces.

This paper describes a physically embodied and animated user interface to an interactive call handling agent, consisting of a small wireless animatronic device in the form of a squirrel, bunny, or parrot. A software tool creates movement primitives, composes these primitives into complex behaviors, and triggers these behaviors dynamically at state changes in the conversational agent's finite state machine. Gaze and gestural cues from the animatronics alert both the user and co-located third parties of incoming phone calls, and data suggests that such alerting is less intrusive than conventional telephones.