IEEE RESEARCH PAPER ON SKINPUT TECHNOLOGY

Moving the sensor above the elbow reduced accuracy to This is unsurprising given the morphology of the arm, with a high degree of bilateral symmetry along the long axis. However, this work was never formally evaluated, as is constrained to finger motions in one hand. Foremost, most mechanical sensors are engineered to provide relatively flat response curves over the range of frequencies that is relevant to our signal. An average of these ratios 1 feature is also included.

Any interactive features bound to that event are fired. Last but not the least, I acknowledge my friends for their contribution in the completion of the seminar report. Help Center Find new research papers in: These include single-handed gestures, taps with different parts of the finger, and differentiating between materials and objects. At present, however, this approach typically requires expensive amplification systems and the application of conductive gel for effective signal acquisition, which would limit the acceptability of this approach for most users. Some energy is radiated into the air as sound waves; this energy is not captured by the Skinput system.

ieee research paper on skinput technology

Third, it classified these input instances. Thus most sensors in this category were not especially sensitive to lower-frequency signals e. This approach is feasible, but suffers from serious occlusion and accuracy limitations. A point FFT for all ten channels, although only the lower ten values are used representing the tfchnology power from 0Hz to Hzyields features.

Few external input devices can claim this accurate, eyes-free input characteristic and papre such a large interaction area. This approach provides an always available, naturally portable, and on-body finger input system.

This is an attractive area to appropriate as it provides considerable surface area for interaction, including a contiguous and flat area for projection.

Data was then sent from our thin client over a local socket to our primary application, written skimput Java. However, the sensors are highly responsive to idee perpendicular to the skin plane perfect for capturing transverse surface waves and longitudinal waves emanating from interior structures.

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In our prototype system, we choose to focus on the arm although the technique could be applied elsewhere. Finally, our sensor design is relatively inexpensive and can be manufactured in a very small form factor e. Our software uses the implementation provided in the Weka machine learning toolkit. However, these transducers were engineered for very different applications than measuring acoustics transmitted through the human body.

ieee research paper on skinput technology

Help Center Find new research papers in: Speech input is a logical choice ieeee always- available input, but reseearch limited in its precision in unpredictable acoustic environments, and suffers from privacy and scalability issues in shared environments.

We tuned the upper sensor package to be more sensitive to lower frequency signals, as these were more prevalent in fleshier areas. Techniques based on computer vision are popular. It should be noted, however, that other, more sophisticated classification techniques and features could be employed.

ieee research paper on skinput technology

Inspection of the confusion matrices showed no systematic errors in the classification, with errors tending to be evenly distributed over the technolog digits. These, however, are computationally expensive and error prone in mobile scenarios where, e. Signals simply diminished in intensity overtime.

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For example, the ATmega processor employed by the Arduino platform can sample analog readings at 77 kHz with no loss of precision, and could therefore provide the full sampling power required for Skinput 55 kHz total.

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.

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.

When shot with a high- speed camera, these appear as ripples, which propagate outward from the point of contact see video. Additionally, the cantilevered sensors were naturally insensitive to forces parallel to the skin e. We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study.

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Last but not the least, I acknowledge my friends for their contribution in the completion of the seminar report. Bone conduction headphones send sound through the bones of the skull and jaw directly to the inner ear, bypassing transmission of sound through the air and outer ear, leaving an unobstructed path for environmental sounds.

While bone conduction microphones might seem skinpuf 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.

Skinput: appropriating the body as an input surface

This suggests there are only limited acoustic continuities between the fingers. Based on pilot data collection, we selected a different set of resonant frequencies for each sensor package.

However, there is one surface that has been previous overlooked as an input canvas and one that happens to always travel with us our skin. In general, tapping on soft regions of the arm creates higher amplitude transverse waves than tapping on boney areas e. For skinpkt, Glove-based input systems allow users to retain most of their natural hand movements, but are cumbersome, uncomfortable, and disruptive to tactile sensation.

Thus, the results presented are to be considered a baseline. This is not surprising, as this condition placed the sensors closer to the input targets than the other conditions.