"A meek endeavor to the triumph" by Sampath Jayarathna

Thursday, December 09, 2010

Reading #29: Scratch Input Creating Large, Inexpensive, Unpowered and Mobile Finger Input Surfaces




              In this paper, the authors provide a new input technique that allows small devices to appropriate existing, large, passive surfaces such as desks and walls, for use as a kind of input device. This Scratch Input technique operates by listening to the sound of “scratching” (e.g., with a fingernail) that is transmitted through the surface material. This signal can be used to recognize a vocabulary of gestures carried out by the user. The proposed sensor is simple and inexpensive, and can be easily incorporated into mobile devices, enabling them to appropriate whatever solid surface they happen to be resting on. Alternately, it can be very easily deployed, for example, to make existing walls or furniture input-capable.

            To capture sound transmission through solid materials, authors proposed to use a modified stethoscope. This is particularly well suited to both amplifying sound and detecting high frequency noises. This is attached to a generic microphone, which converts the sound into an electrical signal. In this particular implementation, the signal is amplified and connected to a computer through the audio-input jack. Scratch Input’s non-spatial property gives it a significantly different character from many other surface input techniques and does preclude some uses. Results indicate participants were able to achieve an average accuracy of 89.5%. As hypothesized, accuracy suffered as gesture complexity grew. Gestures with two of fewer motions achieved accuracies in excess of 90%. 


            The Scratch Input, an acoustic-based finger input technique that can be used to create large, inexpensive and mobile finger input surfaces. This can allow mobile devices to appropriate surfaces on which they rest for gestural input. This revealed that Scratch Input is both easy to use and accurate on a variety of surfaces. Foremost, most mechanical sensors are engineered to provide relatively flat response curves over the range of frequencies that is relevant to signal. This is a desirable property for most applications where a faithful representation of an input signal – uncolored by the properties of the transducer – is desired. However, because only a specific set of frequencies is conducted through the arm in response to tap input, a flat response curve leads to the capture of irrelevant frequencies and thus to a high signal-to-noise ratio.


Jonathan H. said...

I can't say I like the idea of computer that draws for me. That's okay when I don't want to draw, but I would find such a feature very frustrating when I want to create something original. Still, if the author is concentrating on mobile applications, he's probably got the right idea. There isn't a lot of room on phone screen for artistic creativity.

arshad said...

Hi its really very nice blog,very useful information..Mobiles

chris aikens said...

I like this paper and the authors' general ideas of Sound Recognition. They used scratches as input because they occur at a certain frequency range, thus allowing them to quickly cut out noise.