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. Techniques based on computer vision are popular. A full description of SVMs is beyond the scope of this paper. Finally, our sensor design is relatively inexpensive and can be manufactured in a very small form factor e. Additionally, the cantilevered sensors were naturally insensitive to forces parallel to the skin e. In general, tapping on soft regions of the arm creates higher amplitude transverse waves than tapping on boney areas e. In our prototype system, we choose to focus on the arm although the technique could be applied elsewhere.
Bones are held together by ligaments, and joints often include additional biological structures such as fluid cavities. Additionally, the cantilevered sensors were naturally insensitive to forces parallel to the skin e. This makes joints behave as acoustic filters. The primary goal of Skinput is to provide an always available mobile input system that is, an input system that does not require a user to carry or pick up a device. Appropriating the human body as an input device is appealing not only because we have roughly two square meters of external surface area, but also because much of it is easily accessible by our hands e.
Subsequent feature selection established the all-pairs amplitude ratios and certain bands of the FFT to be the most predictive features. Finally, our sensor design is relatively inexpensive and can be manufactured in a very small form factor e. These, however, are computationally expensive and error prone in mobile scenarios where, e. In general, tapping on soft regions of the arm creates higher amplitude transverse waves than tapping on boney areas e.
Tecunology approach is feasible, but suffers from serious occlusion and accuracy limitations. Our software uses the implementation provided in the Weka machine learning toolkit.
This makes joints behave as acoustic filters. We conclude with descriptions of several prototype applications that demonstrate the rich design space we believe Skinput enables. Other approaches have taken the form of wearable computing.
If start and tehcnology 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.
To further illustrate the utility of our approach, we conclude with several proof-of-concept ieeee we developed. Before the SVM can classify input instances, it must first be trained to the user and the sensor position.
We collect these signals using a ifee array of sensors worn as an armband. Thus most sensors in this category were not especially sensitive to lower-frequency signals e.
Skinput: appropriating the body as an input surface
An average of these ratios 1 feature is also included. Bones are held together by ligaments, and joints often include additional biological structures such as fluid cavities.
In our prototype system, we choose to focus on the arm although the technique could be applied elsewhere. These are normalized by the highest-amplitude FFT value found on any channel.
Classification accuracy for the ten-location forearm condition stood at When using Skinput to recognize live input, the same acoustic features are computed on-the fly for each segmented input.
The only potential exception to this was in the technlogy of the pinky, where the ring finger constituted For example, describes a technique that allows a small mobile device to turn tables on which it rests into a gestural finger input canvas. This is almost certainly related to the acoustic loss at the elbow joint and the additional 10cm of distance between the sensor and input targets.
Ieee research paper on skinput technology – Google Docs
A person might walk toward their home, tap their palm to unlock the door and then tap some virtual buttons on their arms to turn on the TV and start flipping through channels. 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.
To iree this acoustic information, we developed a wearable armband that is non- invasive and easily removable. Data was then sent from our thin client over a local socket to our primary application, written in Java. It describes a novel, wearable bio-acoustic sensing array that we built into an armband in order to detect and localize finger taps on the forearm and hand.
Skinput: appropriating the body as an input surface – Semantic Scholar
Skip to main content. Segmentation, as in other conditions, was essentially perfect. Paepr on pilot data collection, we selected a different set of resonant frequencies for each sensor package. 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.
As such, we found them to be lacking in several significant ways.
Furthermore, proprioception our sense of how our body is configured in three-dimensional space iieee us to accurately interact with our bodies in an eyes-free manner. When the sensor was placed below the elbow, on the forearm, one package was tehcnology 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.
A point FFT for all ten channels, although only the lower ten values are used representing the acoustic power from 0Hz to Hzyields features.