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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This excitation vibrates soft tissues surrounding the entire length of the bone, resulting in new longitudinal waves that propagate outward to the skin. We highlight these two separate forms of conduction transverse waves moving directly along the arm surface, and longitudinal waves moving into and out of the bone through soft tissues because these mechanisms carry energy at different frequencies and over different distances.
Roughly speaking, higher frequencies propagate more readily through bone than through soft tissue, and bone conduction carries energy over larger distances than soft tissue conduction. While we do not explicitly model the specific mechanisms of conduction, or depend on these mechanisms for our analysis, we do believe the success of our technique depends on the complex acoustic patterns that result from mixtures of these modalities.
Similarly, we also believe that joints play an important role in making tapped locations acoustically distinct.
Bones are held together by ligaments, and joints often include additional biological structures such as fluid cavities. This makes joints behave as acoustic filters. In some cases, these may simply dampen acoustics; in other cases, these will selectively attenuate specific frequencies, creating location specific acoustic signatures.
However, these transducers were engineered for very different applications than measuring acoustics transmitted through the human body.
As such, we found them to be lacking in several significant ways. Foremost, most mechanical sensors are engineered to provide relatively flat response curves over the range of frequencies that is relevant to our 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. 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. Thus most sensors in this category were not especially sensitive to lower-frequency signals e.
To overcome these challenges, the idea of a single sensing element with a flat response curve, to an array of highly tuned vibration sensors was dropped. By adding small weights to the end of the cantilever, we are able to alter the resonant frequency, allowing the sensing element to be responsive to a unique, narrow, low-frequency band of the acoustic spectrum.
Adding more mass lowers the range of excitation to which a sensor responds; we weighted each element such that it aligned with particular frequencies that pilot studies showed to be useful in characterizing bio-acoustic input.
Figure shows the response curve for one of our sensors, tuned to a resonant frequency of 78Hz. Additionally, the cantilevered sensors were naturally insensitive to forces parallel to the skin e. Thus, the skin stretch induced by many routine movements e. However, the sensors are highly responsive to motion perpendicular to the skin plane perfect for capturing transverse surface waves and longitudinal waves emanating from interior structures.
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.
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.
Published on Oct 10, The Microsoft company have developed Skinput, a technology that appropriates the human body for acoustic transmission, allowing the skin to be used as an input surface.
In particular, we resolve the location of finger taps on the arm and hand by analyzing mechanical vibrations that propagate through the body.
We collect these signals using a novel array of sensors worn as an armband. This approach provides an always available, naturally portable, and on-body finger input system.
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.
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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.
Direct manipulation can be furnished when augmented with a pico-projector. Basic Principles of Skinput Technology.