Skinput Technology Seminar | PPT | PDF Report: Skinput Technology is a modern input sensing applied science which plays the role of an. I. INTRODUCTION. Skin put is a technology which uses the surface of the skin as an input device. Our skin produces natural and distinct mechanical vibrations. PDF | We present Skinput, a technology that appropriates the hu- man body for acoustic transmission, allowing the skin to be used as an input surface.
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Request PDF on ResearchGate | Skinput | Skinput is a technology that appropriates the skin as an input surface by analyzing mechanical vibrations that . We present Skinput, a technology that appropriates the hu- man body for acoustic transmission, allowing the skin to be used as an input surface. Skinput Technology - Download as Word Doc .doc), PDF File .pdf), Text File .txt ) or read online.
We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study. To further illustrate the utility of our approach, we conclude with several proof-of-concept applications we developed. SlideShare Explore Search You. Submit Search. Successfully reported this slideshow.
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Skinput Technology. Upcoming SlideShare. Like this presentation? Why not share! Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Finally, our sensor design is relatively inexpensive and can be manufactured in a very small form factor e.
The decision to have two sensor packages was motivated by our focus on the arm for input. In particular, when placed on the upper arm above the elbow , we 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. When the sensor was placed below the elbow, on the forearm, one package was located near the Radius, the bone that runs from the lateral side of the elbow to the thumb side of the wrist, and the other near the Ulna, which runs parallel to this on the medial side of the arm closest to the body.
Each location thus provided slightly different acoustic coverage and information, helpful in disambiguating input location. Based on pilot data collection, we selected a different set of resonant frequencies for each sensor package. We tuned the upper sensor package to be more sensitive to lower frequency signals, as these were more prevalent in fleshier areas.
Conversely, we tuned the lower sensor array to be sensitive to higher frequencies, in order to better capture signals transmitted though denser bones. This reduced sample rate and consequently low processing bandwidth makes our technique readily portable to embedded processors.
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. Data was then sent from our thin client over a local socket to our primary application, written in Java.
Skinput Technology Seminar | PPT | PDF Report
This program performed three key functions. First, it provided a live visualization of the data from our ten sensors, which was useful in identifying acoustic features. Second, it segmented inputs from the data stream into independent instances taps. Third, it classified these input instances. The audio stream was segmented into individual taps using an absolute exponential average of all ten channels.
When an intensity threshold was exceeded, the program recorded the timestamp as a potential start of a tap.
If start and end crossings were detected that satisfied these criteria, the acoustic data in that period plus a 60ms buffer on either end was considered an input event. Although simple, this heuristic proved to be highly robust, mainly due to the extreme noise suppression provided by sensing approach.
After an input has been segmented, the waveforms are analyzed. The highly discrete nature of taps i.
Signals simply diminished in intensity overtime. Thus, features are computed over the entire input window and do not capture any temporal dynamics.
Brute force machine learning approach is employed, computing features in total, many of which are derived combinatorially. For gross information, the average amplitude, standard deviation and total absolute energy of the waveforms in each channel 30 features is included.
From these, average amplitude ratios between channel pairs 45 features are calculated. An average of these ratios 1 feature is also included. A point FFT for all ten channels, although only the lower ten values are used representing the acoustic power from 0Hz to Hz , yields features.
These are normalized by the highest-amplitude FFT value found on any channel. Also the center of mass of the power spectrum within the same 0Hz to Hz range for each channel, a rough estimation of the fundamental frequency of the signal displacing each sensor 10 features are included.
Subsequent feature selection established the all-pairs amplitude ratios and certain bands of the FFT to be the most predictive features. A full description of SVMs is beyond the scope of this paper.
Our software uses the implementation provided in the Weka machine learning toolkit. It should be noted, however, that other, more sophisticated classification techniques and features could be employed. Thus, the results presented are to be considered a baseline. Before the SVM can classify input instances, it must first be trained to the user and the sensor position. This stage requires the collection of several examples for each input location of interest.
When using Skinput to recognize live input, the same acoustic features are computed on-the fly for each segmented input. These are fed into the trained SVM for classification. Once an input is classified, an event associated with that location is instantiated.
Any interactive features bound to that event are fired. Segmentation, as in other conditions, was essentially perfect. Inspection of the confusion matrices showed no systematic errors in the classification, with errors tending to be evenly distributed over the other digits. When classification was incorrect, the system believed the input to be an adjacent finger This suggests there are only limited acoustic continuities between the fingers.
The only potential exception to this was in the case of the pinky, where the ring finger constituted This is not surprising, as this condition placed the sensors closer to the input targets than the other conditions. The theoretical review explains about the following parameters: The Principle of Skinput Technology: The skinput technology works on the principle of bio-acoustic. Whenever there is a tap of a finger on the skin then the impact of that tap generates acoustic signals.
These generated acoustic signals can be captured with the aid of a device which is a bio-acoustic sensing machine. The Little amount of energy is lost in the form of sound waves to the external environment.
The amplitude on the soft surface like forearm is larger when compared with the amplitude on the hard surface like an elbow.
The amplitude of the wave changes with the force of disturbance. The different acoustic locations of signals are sensed, further operations are done and they are classified by using software.
The different acoustic locations of signals are produced due to changes in the density of bone, size, and distinct filtering effects which are produced by soft tissues and joints.Second, it segmented inputs from the data stream into independent instances taps. Learn how your comment data is processed.
We conclude with descriptions of several prototype applications that demonstrate the rich design space we believe Skinput enables.
Similarly, we also believe that joints play an important role in making tapped locations acoustically distinct.
Embeds 0 No embeds. This is an attractive area to appropriate as it provides considerable surface area for interaction, including a contiguous and flat area for projection. Our skin generates natural and different mechanical vibrations when tapped at distinct areas of the skin.