論文アブストラクト： Despite the ubiquity of touch-based input and the availability of increasingly computationally powerful touchscreen devices, there has been comparatively little work on enhancing basic canonical gestures such as swipe-to-pan and pinch-to-zoom. In this paper, we introduce transient pan and zoom, i.e. pan and zoom manipulation gestures that temporarily alter the view and can be rapidly undone. Leveraging typical touchscreen support for additional contact points, we design our transient gestures such that they co-exist with traditional pan and zoom interaction. We show that our transient pan-and-zoom reduces repetition in multi-level navigation and facilitates rapid movement between document states. We conclude with a discussion of user feedback, and directions for future research.
論文アブストラクト： Paper continues to be a versatile and indispensable material in the 21st century. Of course, paper is a passive medium with no inherent interactivity, precluding us from computationally-enhancing a wide variety of paper-based activities. In this work, we present a new technical approach for bringing the digital and paper worlds closer together, by enabling paper to track finger input and also drawn input with writing implements. Importantly, for paper to still be considered paper, our method had to be very low cost. This necessitated research into materials, fabrication methods and sensing techniques. We describe the outcome of our investigations and show that our method can be sufficiently low-cost and accurate to enable new interactive opportunities with this pervasive and venerable material.
論文アブストラクト： Digital pens emit ink on paper and digitize handwriting. The range of the pen is typically limited to a special writing surface on which the pen's tip is tracked. We present Pentelligence, a pen for handwritten digit recognition that operates on regular paper and does not require a separate tracking device. It senses the pen tip's motions and sound emissions when stroking. Pen motions and writing sounds exhibit complementary properties. Combining both types of sensor data substantially improves the recognition rate. Hilbert envelopes of the writing sounds and mean-filtered motion data are fed to neural networks for majority voting. The results on a dataset of 9408 handwritten digits taken from 26 individuals show that motion+sound outperforms single-sensor approaches at an accuracy of 78.4% for 10 test users. Retraining the networks for a single writer on a dataset of 2120 samples increased the precision to 100% for single handwritten digits at an overall accuracy of 98.3%.
論文アブストラクト： End-to-end latency corresponds to the temporal difference between a user input and the corresponding output from a system. It has been shown to degrade user performance in both direct and indirect interaction. If it can be reduced to some extend, latency can also be compensated through software compensation by trying to predict the future position of the cursor based on previous positions, velocities and accelerations. In this paper, we propose a hybrid hardware and software prediction technique specifically designed for partially compensating end-to-end latency in indirect pointing. We combine a computer mouse with a high frequency accelerometer to predict the future location of the pointer using Euler based equations. Our prediction method results in more accurate prediction than previously introduced prediction algorithms for direct touch. A controlled experiment also revealed that it can improve target acquisition time in pointing tasks.