Mirrors have been a core feature in ballet studios for over five hundred years. While physical mirrors provide real-time feedback, they do not inform dancers of their errors. Thus, technologies such as motion tracking have been used to augment what a physical mirror can provide. Current augmented mirrors, however, only implement one mode of communication, usually visual, and do not provide a holistic feedback to dancers that includes all the feedback elements commonly used in ballet classes. We conducted a mixed-method study with 16 novices and 16 expert dancers in which we compared two different modes of communication (visual and verbal), two different types of feedback (value and corrective) and two levels of guidance (mirror, or no mirror). Participants' ballet technique scores were evaluated by a remote teacher on eight ballet combinations (tendue, adagio, pirouette, saute, plié, degage, frappe and battement tendue). We report quantitative and qualitative results that show how the level of guidance, mode of communication, and type of feedback, needs to be tuned in different ways for novices and experts.

Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.

Museums are increasingly embracing technologies that provide highly-individualized and highly-interactive experiences to visitors. With embodied interaction experiences, increased localization accuracy supports greater nuance in interaction design, but there is usually a tradeoff between fast, accurate tracking and the ability to preserve the identity of users. Customization of experience relies on the ability to detect the identity of visitors, however. We present a method that combines fine-grained indoor tracking with robust preservation of the unique identities of multiple users. Our model merges input from an RFID reader with input from a commercial camera-based tracking system. We developed a probabilistic Bayesian model to infer at run-time the correct identification of the subjects in the camera's field of view. This method, tested in a lab and at a local museum, requires minimal modification to the exhibition space, while addressing several identity-preservation problems for which many indoor tracking systems do not have robust solutions.

The wide availability of body-sensing technologies (such as Nintendo Wii and Microsoft Kinect) has the potential to bring full-body interaction to the masses, but the design of hand gestures and body movements that can be easily discovered by the users of such systems is still a challenge. In this paper, we revise and evaluate Framed Guessability, a design methodology for crafting discoverable hand gestures and body movements that focuses participants' suggestions within a "frame," i.e. a scenario. We elicited gestures and body movements via the Guessability and the Framed Guessability methods, consulting 89 participants in-lab. We then conducted an in-situ quasi-experimental study with 138 museum visitors to compare the discoverability of gestures and body movements elicited with these two methods. We found that the Framed Guessability movements were more discoverable than those generated via traditional Guessability, even though in the museum there was no reference to the frame.

As data rather than physical artifacts become more commonly the product of modern scientific endeavor, we must attend to humandata interactions as people reason about and with representations of data increasingly being presented in museum settings. Complex data sets can be impenetrable for novices, so the exhibit presented here was designed to give visitors control over a personalized "slice" of the data set as an entry point for exploration. Personalized control and collaboration can often be at odds in exhibits, however. This paper presents a study of two alternate approaches to designing an embodied interaction control for the exhibit that serves both needs. The results demonstrate that interaction design can affect children's perspective taking as they interact with a Census data map museum display, and that the perspective taken by individuals is correlated with their operation of the interactive exhibit and the kinds of reasoning they employ while investigating data.