Then we will describe the Skinput sensor and the processing techniques we use to segment, analyze, and classify bio-acoustic signals. For example, determining whether, e. For example, Glove-based input systems allow users to retain most of their natural hand movements, but are cumbersome, uncomfortable, and disruptive to tactile sensation. Additionally, the cantilevered sensors were naturally insensitive to forces parallel to the skin e. Inspection of the confusion matrices showed no systematic errors in the classification, with errors tending to be evenly distributed over the other digits. To capture this acoustic information, we developed a wearable armband that is non- invasive and easily removable.
Third, it classified these input instances. Similarly, we also believe that joints play an important role in making tapped locations acoustically distinct. Help Center Find new research papers in: For example, we can readily flick each of our fingers, touch the tip of our nose, and clap our hands together without visual assistance. Based on pilot data collection, we selected a different set of resonant frequencies for each sensor package. The only potential exception to this was in the case of the pinky, where the ring finger constituted To capture this acoustic information, we developed a wearable armband that is non- invasive and easily removable.
Finally, our sensor design is relatively inexpensive and can be manufactured in a very small form factor e. In general, tapping on soft regions of the arm creates higher amplitude transverse waves than tapping on boney areas e.
Skinput: appropriating the body as an input surface
Techniques based paaper computer vision are popular. 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 sminput to the Humerus, the main bone that runs from shoulder to elbow. The amplitude of these ripples is correlated to both the tapping force and to the volume and compliance of soft tissues under the impact area. 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.
For gross information, the average amplitude, standard deviation and reesarch absolute energy of the waveforms in each channel 30 features is included. Enter the email address you signed up with and we’ll email you a reset link.
This suggests there are only skiput acoustic continuities between the fingers. An average of these ratios 1 feature is also included.
Chris Harrison | Skinput
The decision to have two sensor packages was motivated by our focus on the arm for input. However, these transducers were engineered for very different applications than measuring acoustics transmitted through the human body.
It should be noted, however, that other, more sophisticated classification techniques and features could be employed. This is not surprising, as this condition placed the sensors closer to the input targets than the other conditions.
One option is to opportunistically appropriate surface area from the environment for interactive purposes. By adding small weights to the end of the cantilever, we are able to alter the resonant frequency, allowing the sensing element to kn responsive to a unique, narrow, low-frequency band of the acoustic spectrum.
To further illustrate the utility of our approach, we conclude with several proof-of-concept applications we developed. We collect these signals using a novel array of sensors worn papwr an armband. 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. Skinpu primary goal of Skinput is to papee an always available mobile input system that is, an input system that does not require a user to carry or pick up a device. For example, determining whether, e. Data was then sent from our thin client over a local socket to our primary application, written in Java. I take this opportunity to express my gratitude to the people who have been instrumental in the successful completion of this report.
Among the acoustic energy transmitted through the arm, the most readily visible are transverse waves, created by the displacement of the skin from a finger impact Figure 2. To capture the rich variety of acoustic information described in the previous section, we evaluated many sensing technologies, including bone reseach microphones, conventional microphones coupled with stethoscopes, piezo contact microphones, and accelerometers.
Researchers have harnessed the electrical signals generated by muscle activation during normal hand movement through electromyography EMG. I also do not like to miss the opportunity to acknowledge the contribution of all dignitary Staff-members of Nalla Malla Reddy Engineering College for their kind assistance and cooperation during the development of my Seminar report.
Thus, features are computed over the entire input window and do not capture any temporal dynamics. If start paped end crossings were detected that satisfied these criteria, the acoustic data in that period plus a 60ms buffer on either end was rwsearch an input event.
Although simple, this heuristic proved to be highly robust, mainly due to the extreme noise suppression provided by sensing approach. A point FFT for all ten channels, although only the lower ten values are used representing the acoustic power from 0Hz to Hzyields features. 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.
Similarly, we also believe that joints play an important role in making tapped locations acoustically distinct. First, it provided a live visualization of the data from our ten sensors, which was useful in identifying acoustic features.