Help Center Find new research papers in: These features are generally subconsciously driven and cannot be controlled with sufficient precision for direct input. Enter the email address you signed up with and we’ll email you a reset link. Skinput leverages the natural acoustic conduction properties of the human body to provide an input system, and is thus related to previous work in the use of biological signals for computer input. 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. This is an attractive area to appropriate as it provides considerable surface area for interaction, including a contiguous and flat area for projection. Similarly, we also believe that joints play an important role in making tapped locations acoustically distinct.
However, their small size typically leads to limited interaction space e. This effect was more prominent laterally than longitudinally. This excitation vibrates soft tissues surrounding the entire length of the bone, resulting in new longitudinal waves that propagate outward to the skin. Bone conduction microphones are typically worn near the ear, where they can sense vibrations propagating from the mouth and larynx during speech. Few external input devices can claim this accurate, eyes-free input characteristic and provide such a large interaction area. Some energy is radiated into the air as sound waves; this energy is not captured by the Skinput system.
Skinput: appropriating the body as an input surface
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. Each location thus provided slightly different acoustic coverage and information, helpful in disambiguating input location. This stage requires the collection of several examples for each input location of interest. Skinput leverages the natural acoustic conduction properties of the human body to provide an input system, and is thus related to previous work in the use of biological signals for computer input.
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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.
These, however, are computationally expensive and error prone in mobile scenarios where, e. Bone conduction headphones send sound through the bones of the skull and ressarch directly to the inner ear, bypassing transmission of sound through the air and outer ear, leaving an unobstructed path for environmental sounds.
Once an input is classified, an event associated with that location is instantiated. The only potential exception to this was in the case of the pinky, where the ring finger constituted 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. Help Center Find new research papers in: This program performed three key functions.
This search revealed one plausible, although irregular, layout with high accuracy at six input locations. This is not surprising, as this condition placed the sensors closer to the input targets than the other conditions.
Other approaches have taken the form of wearable computing. First, it provided a live visualization of the data from our ten sensors, which was useful in identifying acoustic features. We tuned the upper sensor package to be more sensitive to lower frequency signals, as these were more prevalent in fleshier areas.
One option is to opportunistically appropriate surface area from the environment for interactive purposes. This is unsurprising given the morphology of the arm, with a high degree of bilateral symmetry along the long axis. Inspection of texhnology confusion matrices showed no systematic errors in the classification, with errors tending to be evenly distributed over the other digits.
The decision to have two sensor packages was motivated by our focus on the arm for input.
However, tables are not always present, skinpt 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.
In this section, we discuss the mechanical phenomenon that enables Skinput, with a specific focus on the mechanical properties of the arm. The eyes-free input condition yielded lower accuracies than other conditions, averaging Then we will describe the Skinput sensor and the processing techniques we use to segment, analyze, and classify bio-acoustic signals.
In our prototype system, we choose to focus on the arm although the technique could be applied elsewhere. For example, we can readily flick each of our fingers, touch the tip of our nose, and clap our hands together without visual assistance. For gross information, the average amplitude, standard deviation and total absolute energy of the waveforms in each channel 30 features is included.
Third, it classified these input instances. 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.
While we do not explicitly model the specific mechanisms of conduction, or depend on these mechanisms for our analysis, we do believe the success reseaarch our technique depends on the complex acoustic patterns that result from mixtures of these modalities. 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.
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This reduced sample rate and consequently low processing bandwidth makes our technique readily portable to embedded processors. 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.
Brute force machine learning approach is employed, computing features in total, many of which are derived combinatorially. We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study.