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. When classification was incorrect, the system believed the input to be an adjacent finger The below- elbow placement performed the best, posting a Researchers have harnessed the electrical signals generated by muscle activation during normal hand movement through electromyography EMG. Thus most sensors in this category were not especially sensitive to lower-frequency signals e. For example, describes a technique that allows a small mobile device to turn tables on which it rests into a gestural finger input canvas. When an intensity threshold was exceeded, the program recorded the timestamp as a potential start of a tap.
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. This is unsurprising given the morphology of the arm, with a high degree of bilateral symmetry along the long axis. Foremost, most mechanical sensors are engineered to provide relatively flat response curves over the range of frequencies that is relevant to our signal. From these, average amplitude ratios between channel pairs 45 features are calculated. Our approach is also inspired by systems that leverage acoustic transmission through non-body input surfaces. 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.
Other approaches have taken the form of wearable computing. We conclude with descriptions of several prototype applications that demonstrate the rich design space we believe Skinput enables.
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. Ramchandra, Head of the Department, for giving me a chance to present this seminar. If start and end crossings were detected that satisfied these criteria, the acoustic researrch in that period plus a 60ms buffer on either end was considered an input event.
Skinput: appropriating the body as an input surface – Semantic Scholar
Segmentation, as in other conditions, was essentially perfect. This stage requires the collection of several examples for each input location of interest. The audio stream was segmented into individual taps using an absolute exponential average of all ten channels.
When classification was incorrect, the system believed the input to be an adjacent finger Moreover, both techniques required the placement of sensors near the paer of interaction e.
A full description of SVMs is beyond the scope of this paper. Researchers have harnessed the electrical signals generated by muscle activation during normal hand movement through electromyography EMG.
This approach is feasible, but suffers from serious occlusion and accuracy limitations.
The input technology most related to our own is that of Amento et al. Techniques based on computer vision are popular.
(DOC) SKINPUT TECHNOLOGY | Sai Dheeraj Reddy –
For gross information, the average amplitude, standard deviation and total absolute energy of pa;er waveforms in each channel 30 features is included. Although simple, this heuristic proved to be highly robust, mainly due to skniput extreme noise suppression provided by sensing approach. 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.
Appropriating the Body as an Input Surface Devices with significant computational power and capabilities can now be easily carried on our bodies.
One option is to opportunistically appropriate surface area from the environment for interactive purposes. So in a few years time, with Skinput, computing is always available: However, these transducers were engineered for very different applications than measuring acoustics transmitted through the human body.
Thus, the results presented are to be considered a baseline. These are normalized by the highest-amplitude FFT value found on any channel.
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. For example, we can readily flick each of our fingers, touch the tip of our nose, and clap our hands together without visual assistance.
In addition to the energy that propagates on the surface of the arm, some energy is transmitted inward, toward the skeleton Figure 3.
Skinput: appropriating the body as an input surface
The decision to ppaper two sensor packages was motivated by our focus on the arm for input. Speech input is a logical choice for always- available input, but is limited in its precision in unpredictable acoustic environments, and suffers from privacy and scalability issues in shared environments.
To capture the rich variety of acoustic information described in the previous section, we evaluated many sensing technologies, including bone conduction microphones, conventional microphones coupled with stethoscopes, piezo contact microphones, and accelerometers.
Subsequent feature selection established the all-pairs amplitude ratios and certain bands of the FFT to be the most predictive features. Classification accuracy for the ten-location forearm condition stood at For example, Glove-based input systems allow users to retain most of their natural hand movements, but are cumbersome, uncomfortable, and psper to tactile sensation.
This approach provides an always available, naturally portable, and on-body finger input system. However, there is one surface that has been previous overlooked as an input canvas, and one that happens to always travel with us: