Figure 1. Morphees are self-actuated flexible mobile devices that adapt their shapes to offer better affordances. (a) E.g a mobile device can shift into a console-like shape by curling two opposite edges and be easily grasped with two hands. Among the six strategies we built to actuate Morphees, here are two high-fidelity prototypes using Shape Memory Alloys (SMA): (b) one using projection and tracking on wood tiles that are actuated with thin SMA wires; and (c) one directly bending a flexible touchscreen (E-Ink and Unmousepad) by using (d) SMA wires that we educated (forged) to remember the shape we needed. ABSTRACT We introduce the term shape resolution, which adds to the existing definitions of screen and touch resolution. We propose a framework, based on a geometric model (Non-Uniform Rational B-splines), which defines a metric for shape resolution in ten features. We illustrate it by comparing the current related work of shape changing devices. We then propose the concept of Morphees that are self-actuated flexible mobile devices adapting their shapes on their own to the context of use in order to offer better affordances. For instance, when a game is launched, the mobile device morphs into a console-like shape by curling two opposite edges to be better grasped with two hands. We then create preliminary prototypes of Morphees in order to explore six different building strategies using advanced shape changing materials (dielectric electro active polymers and shape memory alloys). By comparing the shape resolution of our prototypes, we generate insights to help designers toward creating high shape resolution Morphees.

We present a flexible Machine Learning approach for learning user-specific touch input models to increase touch accuracy on mobile devices. The model is based on flexible, non-parametric Gaussian Process regression and is learned using recorded touch inputs. We demonstrate that significant touch accuracy improvements can be obtained when either raw sensor data is used as an input or when the device's reported touch location is used as an input, with the latter marginally outperforming the former. We show that learned offset functions are highly nonlinear and user-specific and that user-specific models outperform models trained on data pooled from several users. Crucially, significant performance improvements can be obtained with a small (≈ 200) number of training examples, easily obtained for a particular user through a calibration game or from keyboard entry data.

TransPrint is a method for fabricating flexible, transparent free-form displays based on electrochromism. Using screen-printing or inkjet printing of electrochromic ink, plus a straightforward assembly process, TransPrint enables rapid prototyping of displays by nonexperts. The displays are nonlight-emissive and only require power to switch state and support the integration of capacitive touch sensing for interactivity. We present instructions and best practices on how to design and assemble the displays and discuss the benefits and shortcomings of the TransPrint approach. To demonstrate the broad applicability of the approach, we present six application prototypes.

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