• Figure 5: The Proxemic Face as a social entity. (a) The lonely proxemic face. (b) It sees Rob come in and greets him. (c) It looks at Rob when Rob looks at him (d) but is saddened when Rob looks away. (e) Initially fascinated by the flashlight beam, it is annoyed when Rob pokes it in the eye. (f) Rob is a bit too close for comfort.

person entered his office, Greenberg would usually move toward a small table away from his desk (and thus the unit), which degraded the video—Kuzuoka knew that a conversation was occurring but could not see or hear any details. While this explanation is technical, in practice both found this a very easy and socially natural way to interact while still maintaining some privacy and minimizing distraction.

The Social Surface: The Proxemic Face Our next major project on proxemics was created to demonstrate the capabilities of our “Proximity Toolkit” for rapidly prototype proxemic interactions (see sidebar). To test our toolkit, we decided to build a social actor—a caricature—whose behavior was driven by a set of simple rules inspired by Hall’s proxemic zones. The sequence below illustrates some of its behaviors

information and Controls in Hand: Proxemic Presenter

Our next project was more application-oriented. We wanted to see what we could do if we added proximity awareness to a traditional presentation tool (e.g., PowerPoint) running on a vertical surface. We focused on two specific capabilities: We wanted to make it easier for a speaker to access his or her speaker notes, and we wanted to make it easier for a speaker to jump over slides by selecting from a set of overview thumbnails. While existing tools have these capabilities, they usually work best through a second display.

Miaosen Wang created the “Proxemic Presenter” to provide these facilities directly on the single surface. It exploits distance, orientation, and identity (to distinguish the speaker from others). The sequence in Figure 6 shows how it works. (a) When a speaker is facing the audience, the presentation fills

(see Diaz-Marino and Greenberg’s “The Proximity Toolkit and ViconFace: The Video” [ 7]). In Figure 5 we see (a) the proxemic face is lonely when no one is present, (b) happy when its friend comes into the room, (c-d) maintaining eye contact and expression as a function of distance, (d) becoming sad-der as its friend moves or looks away, (e) annoyed when its friend pokes it in the eye, and (f) becoming angry as its friend crosses into his intimate space. The face was also startled by sudden movements and could be distracted by other objects pointed toward it. While the face was just a simple social caricature, visitors to our lab found it immediately understandable and compelling, where they assumed it had much more intelligence and knowledge of social rules than it actually had (its behavior repertoire was really nothing more than a simple state machine).

Prototyping Proxemic Interactions: The Proximity Toolkit

January + February 2011

There are many ways to capture proximity data. Methods include sensors, vision and scene analysis, motion capture via tags, time-of-flight measures, instrumented rooms, depth sensors, and others. No method is yet perfect, as there is a trade-off between important factors such as data accuracy, the type of information returned, equipment costs, difficulty of configuration, and amount of custom coding required to exploit the returned information effectively.

distance, orientation, identity, and movement information as a series of easy-to-program events. Additional information processed from this data is also returned as events, such as the intersection ray of one object facing toward another object, or whether one object has “collided” with another object by crossing a distance threshold. Programming with these events is straight-forward. We found that computer science students, after just an hour of training, could construct simple but quite interesting proximity-aware applications in a very short amount of time (a day or two).

interactions

Because we wanted to concentrate on the design of
proxemic interactions instead of the underlying plumbing,
we built the Proxemity Toolkit. Currently based on the
expensive Vicon Motion Capture system, it tracks particular
objects (via markers) and their proximity relationships
with each other. From that, we generate highly accurate
Figure 13 illustrates one of the controls in this toolkit, where
it is displaying the current state of the living room ecology
described in previous systems. The figure shows the fixed
and semi-fixed features of the room (the room boundaries,
the coach, side table, bookcase, and displays). It also
dynamically shows the several moving entities in the room
and their orientation (a wand and the person by his hat), and
that the person is touching the display. Programmatically, it