When a finger taps the skin, several distinct forms of acoustic energy are produced. Moving the sensor above the elbow reduced accuracy to These features are generally subconsciously driven and cannot be controlled with sufficient precision for direct input. Segmentation, as in other conditions, was essentially perfect. Before the SVM can classify input instances, it must first be trained to the user and the sensor position. For example, Scratch Input is technique that allows a small mobile device to turn tables on which it rests into a gestural finger input canvas.
Each location thus provided slightly different acoustic coverage and information, helpful in disambiguating input location. For example, we can readily flick each of our fingers, touch the tip of our nose, and clap our hands together without visual assistance. Once an input is classified, an event associated with that location is instantiated. While bone conduction microphones might seem a suitable choice for Skinput, these devices are typically engineered for capturing human voice, and filter out energy below the range of human speech whose lowest frequency is around 85Hz. Finally, bone conduction microphones and headphones now common consumer technologies – represent an additional bio- sensing technology that is relevant to the present work. This is an attractive area to appropriate as it provides considerable surface area for interaction, including a contiguous and flat area for projection. To capture the rich variety of acoustic information described in the previous section, we evaluated many sensing technologies, including bone conduction microphones, conventional microphones coupled with stethoscopes, piezo contact microphones, and accelerometers.
However, their small size typically leads to limited interaction space e.
Skinput: appropriating the body as an input surface – Semantic Scholar
This approach is feasible, but suffers from serious occlusion and accuracy limitations. We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study.
Data was then sent from our thin client over a local socket to our primary application, written in Skinpuy. Furthermore, proprioception our sense of how our body is configured in three-dimensional space allows us to accurately interact with our bodies in an eyes-free manner.
Skinput: appropriating the body as an input surface
However, tables are not always present, and in a mobile context, users are unlikely to want to carry appropriated surfaces with them at this point, one might as well just have a larger device. Apart from the efforts of me, the success of this project depends largely on the encouragement and guidelines of many others.
Moving the sensor above the elbow reduced accuracy to Devices with significant computational power and capabilities can now be easily carried on our bodies.
This is an attractive area to appropriate as it provides considerable surface area for interaction, including a contiguous and flat area for projection. Finally, our sensor design is relatively inexpensive and can be manufactured in a very small form factor e. This suggests there are paepr limited acoustic continuities between the fingers.
Conversely, we tuned the lower sensor array to be sensitive to higher frequencies, in order to better capture signals transmitted though denser bones. In this section, we discuss the mechanical phenomenon that enables Skinput, with a specific focus on reesarch mechanical properties of the arm.
The decision to have two sensor packages was motivated by our focus on the arm for input. For pa;er, we soinput readily flick each of our fingers, touch the tip of our nose, and clap our hands together without visual assistance. Techniques based on computer vision are popular. Few external input devices can claim this accurate, eyes-free input characteristic and provide such a large interaction area.
Moreover, both techniques required the placement of sensors near the area of interaction e. When a finger taps the skin, several distinct forms of acoustic energy are produced.
We conclude with descriptions of several prototype applications that demonstrate the rich design space we believe Skinput enables. In addition to the energy that propagates on the surface of the arm, some energy is transmitted inward, toward the skeleton Figure 3.
Chris Harrison | Skinput
It pa;er a novel, wearable bio-acoustic sensing rdsearch that we built into an armband in order to detect and localize finger taps on the forearm and hand. The eyes-free input condition yielded lower accuracies than other conditions, averaging In particular, when placed on the upper arm above the elbowwe hoped to collect acoustic information from the fleshy bicep area in addition to the firmer area on the underside of the arm, with better acoustic coupling to the Humerus, the main bone that runs from shoulder to elbow.
Figure shows the response curve for one oj our sensors, tuned to a resonant frequency of 78Hz. Since we cannot simply make buttons and screens larger without losing the primary benefit of small size, we consider alternative approaches that enhance interactions with small mobile systems. Then we will describe the Skinput sensor and the processing techniques we use to segment, analyze, and classify bio-acoustic signals.
Bone conduction microphones are typically worn near the ear, where they can sense vibrations propagating from the mouth and larynx during speech. Thus, features are computed over the entire input window and do not capture any temporal dynamics. Classification accuracy for pn ten-location forearm skniput stood at From these, average amplitude ratios between channel pairs 45 features are calculated.
However, there is one surface that has been previous overlooked as an input canvas, and one that happens to always travel with us: 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.
For gross information, the average amplitude, standard deviation and total absolute energy of the waveforms in each channel 30 features is included. The only potential exception to this was in the case of the pinky, where the ring finger constituted Bones are held together by ligaments, and joints often include additional biological structures such as fluid cavities.