Thus most sensors in this category were not especially sensitive to lower-frequency signals e. Figure shows the response curve for one of our sensors, tuned to a resonant frequency of 78Hz. Before the SVM can classify input instances, it must first be trained to the user and the sensor position. 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. In general, tapping on soft regions of the arm creates higher amplitude transverse waves than tapping on boney areas e. For gross information, the average amplitude, standard deviation and total absolute energy of the waveforms in each channel 30 features is included.
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. Inspection of the confusion matrices showed no systematic errors in the classification, with errors tending to be evenly distributed over the other digits. Similarly, we also believe that joints play an important role in making tapped locations acoustically distinct. However, this work was never formally evaluated, as is constrained to finger motions in one hand. 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. In contrast, brain signals have been harnessed as a direct input for use by paralyzed patients, but direct brain computer interfaces BCIs still lacks the bandwidth required for everyday computing tasks, and require levels of focus, training, and concentration that are incompatible with typical computer interaction. In this section, we discuss the mechanical phenomenon that enables Skinput, with a specific focus on the mechanical properties of the arm.
So in a few years time, with Skinput, computing is always available: Second, it segmented inputs from the data stream into independent instances taps.
Our software uses the implementation provided in the Weka machine learning toolkit. Figure shows the response curve for one of our sensors, tuned to a resonant frequency of 78Hz. Thus, features are computed over the entire input window and do not capture any temporal dynamics.
Foremost, most mechanical sensors are engineered to provide relatively flat response curves over the range of frequencies that rwsearch relevant to our signal. These include single-handed gestures, taps with different parts of the finger, and differentiating between materials and objects. 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.
I am very grateful to Prof. 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. A full description of SVMs is beyond the scope of this paper.
This approach is feasible, but suffers from serious occlusion and accuracy limitations. Inspection of the confusion matrices showed no systematic errors in the classification, with errors tending to be evenly papwr over the other digits. For example, we can readily flick each of our fingers, touch the tip of our nose, and clap our hands together without visual assistance.
Click here to sign up. The paoer input condition yielded lower accuracies than other conditions, averaging 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.
Skinput: appropriating the body as an input surface
Other approaches have taken the form of wearable computing. In addition to the energy that rseearch on the surface of the arm, some energy is transmitted inward, toward the skeleton Figure 3. These are fed into the trained SVM for classification. The decision to have two sensor packages was motivated by our focus on the arm for eiee. In the present work, we briefly explore the combination of on-body sensing with on- body projection.
This search revealed one plausible, although irregular, layout with high accuracy technologyy six input locations. Moving the sensor above the elbow reduced accuracy to 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.
Ieee research paper on skinput technology – Google Docs
Furthermore, proprioception our sense of how our body is configured in three-dimensional space allows us to accurately interact with our bodies technologu an eyes-free manner. Skinupt 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. 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.
One option is to opportunistically appropriate surface resdarch from the environment for interactive purposes.
This makes joints behave as acoustic filters. Classification accuracy for the ten-location forearm condition stood at However, tables are not always present, and in a mobile context, users are unlikely to want to carry appropriated surfaces with them at this point, one might as well just have a larger device. There has been less work relating to the intersection of finger input and biological signals.
When shot with a high- speed camera, these appear as ripples, which propagate outward from the point of contact see video.