Tap Your Arm: Skinput Turns Skin into a Touchscreen

ARM AS A TOUCHSCREEN

“ARM AS A TOUCHSCREEN”
ABHIJEET S. KAPSE

Table of Contents
Abstract ........................................................................................................................................02
1. Introduction ..............................................................................................................................03
2. Skinput ......................................................................................................................................05
2.1 What is Skinput……………………………………………….……………………….05
2.2 Principle of Skinput……………………………………………….…………………..06

3. Working Of Skinput ................................................................................................................07
3.1 Pico-projector ..................................................................................................................07
3.2 Bioacoustics.....................................................................................................................08
3.2.1 Transverse Wave Propagation………………………….……………………….09
3.2.2 Longitudinal Wave Propagation……………………….……………….……….09
3.2.3 Bioacoustic Sensor…………….……………………….……………….……….10
3.3 Bluetooth .........................................................................................................................11

4. Experiments…………………….………………….……………………..…………………..13

4.1 Experimental Conditions ..................................................................................................13
4.2 Analysis ............................................................................................................................19
4.3 BMI Effect ........................................................................................................................21

5. Advantages………...…………………………………………………………………………23

6. Disadvantages…………..…………………………………………………………………….24

7. Applications…………………………………………………………………………………..25

8. Future Implementation……………………………………………………...………………26

Conclusion……………...……………………………………………………………………….27

References…………………………………………………………………………...…………..28

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ABSTRACT
Popularity of mobiles devices increasing day by day due to the advantages like portability,
mobility and flexibility, but the limited size gives very less interactive surface area. We cannot
just make the device large without losing benefit of small size. So the Microsoft company has
developed Skinput, a technology that appropriates the human body for acoustic transmission,
allowing the skin to be used as an input surface. Human body produces different vibrations when
we tap on different body parts. With the help of this unique property of human body we can use
different locations as different functions of small devices like mobile phones or music players.
When we tap on our body some mechanical vibrations propagates through the body that
vibrations are captured by sensor array and with the help of armband we send the signals
produced by sensors to the mobile devices and the software can detect on which location of our
body part the finger is tapped. So according to the location, desired operation is performed.
When augmented with a Pico-projector, the device can provide a direct manipulation, graphical
user interface on the body. This approach provides an always available, naturally portable, and
on-body finger input system.

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CHAPTER 1
INTRODUCTION
The world is going crazy over an invention, which is known as mobile phones. The Mobile
devices became popular in less time due some advantages they came up with, like portability,
flexibility, mobility and responsiveness. These devices easily get fit in our pocket means we
don’t need to carry any extra surface area with us. Devices with significant computational power
and capabilities can now be easily carried on our bodies. However, their small size typically
leads to limited interaction space (e.g. diminutive i.e. very small screens, buttons, and jog
wheels) and consequently diminishes their usability and functionality. Since, we cannot simply
make buttons and screens larger without losing the primary benefit of small size.
The alternative approaches that enhance interactions with small mobile systems. One option is to
opportunistically appropriate surface area from the environment for interactive purposes. For
example, a technique that allows a small mobile device to turn tables on which it rests into a
gestural finger input canvas. However, tables are not always present so we cannot use these
technique everywhere, 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). 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.
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.g., arms, upper legs, torso).We can use this without any visual contact. Furthermore,
proprioception – our sense of how our body is configured in three-dimensional space – allows us
to accurately interact with our bodies in an eyes-free manner. For example, we can readily flick
each of our fingers, touch the tip of our nose, and clap our hands together without visual
assistance. Few external input devices can claim this accurate, eyes-free input characteristic and
provide such a large interaction area. We can use any part of our body as an input surface but the
for comfortable operation we need to use our arm as an input. In this paper, we present our work

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on Skinput – a method that allows the body to be appropriated for finger input using a novel,
non-invasive, wearable bio-acoustic sensor.
The technology was developed by Chris Harrison, Desney Tan, and Dan Morris, at Microsoft
Research's Computational User Experiences Group. Skinput is a combination of three
technologies which are pico-projector, bioacoustics sensors and Bluetooth. Pico-projector will
display mobile screen on our skin. As according to our need we tap on our body. After tapping
some vibrations are produced through our body, those ripples are captured by bioacoustics
sensors which are mounted armband. These armband is connected to the mobile device by
wireless connection i.e. Bluetooth. Mobile device consists of a software which matches these
vibration signal with the store signals and desired operation is performed. We have use Support
Vector Machine algorithm i.e. supervised learning algorithm to train our software. At initial
stage we have to store the signal data from each location of our arm which is the reference signal
for our software. Skinput employs acoustics, which take advantage of the human body's natural
sound conductive properties (e.g., bone conduction). This allows the body to be annexed as an
input surface without the need for the skin to be invasively instrumented with sensors, tracking
markers, or other items.
The contributions of this paper are: The description of the design of a novel, wearable sensor for
bio-acoustic signal acquisition. Also the description of an analysis approach that enables skinput
system to resolve the location of finger taps on the body. In this paper, we present working on
skinput—a method that allows the body to be appropriated for finger input using a novel, non-
invasive, wearable bio-acoustic sensor. When coupled with a pico-projector, the skin can operate
as an interactive canvas supporting both input and graphical output.

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CHAPTER 2
Skinput
2.1 What Is Skinput
Touch screens have revolutionized the way we communicate with electronics, but sometimes
they can get a little cramped — wouldn’t it be great if the iPhone’s screen was just a little bit
bigger? One creative solution is Skinput, a device that uses a pico projector to beam graphics
(keyboards, menus, etc.) onto a user’s palm and forearm, transforming the skin into a computer
interface. Skinput is a combination of two words i.e Skin and Input. This technology uses largest
part of our body which is skin as an input surface for mobile gadgets. Chris Harrison and team of
Microsoft research has developed Skinput, a way in which your skin can become a touch screen
device or your fingers buttons on a MP3 controller.

Figure 1: Display on palm using Skinput Technology

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Skinput represents one way to decouple input from electronic devices with the aim of allowing
devices to become smaller without simultaneously shrinking the surface area on which input can
be performed.
2.2 Principle Of Skinput

Due to a unique structure of the arm, along with varying bone thickness, muscle or fat tissue
concentrations and the like, each tap in different places along the arm delivers a unique
combination of transverse and longitudinal waves up the arm, to the torso. Transverse waves are
the ripples of lose skin, expanding away from the point of impact. Longitudinal waves are
vibrations emitted by the (recently struck) bone along its entire length, from the center of the arm
towards the skin.

Skinput relies on an armband, currently worn around the biceps. It detects vibrations in the arm
and compares them with predefined control commands (e.g. up, down, back, enter).
Additionally, thanks for the sense of proprioception (the ability to sense the position of our body
parts without looking), Skinput does not preoccupy the user's vision (much like touch typing).

The current Skinput prototype build relies on a series of arrays of small, cantilevered piezo films
(MiniSense100, Measurement Specialties, Inc.). This setup was found favourable for measuring
the specific wave frequencies and providing a satisfactory noise-to-signal ratio. The sensors
output acoustic wave signals, which are then processed, segmented and classified by the software
in order to execute a predefined command.

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CHAPTER 3
Working Of Skinput
3.1 Pico-Projector

Pico projectors are tiny battery powered projectors - as small as a mobile phone - or even
smaller: these projectors can even be embedded inside phones or digital cameras. Pico-projectors
are small, but they can show large displays (sometimes up to 100"). While great for mobility and
content sharing, pico-projectors offer low brightness and resolution compared to larger
projectors. It is a new innovation, but pico-projectors are already selling at a rate of about a
million units a year (in 2010), and the market is expected to continue growing quickly.

Figure 2: Pico-projector

We are using DLP (Digital Light Processing) - pioneered by TI, the idea behind DLP is to use
tiny mirrors on a chip that direct the light. Each mirror controls the amount of light each pixel on

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the target picture gets (the mirror has two states, on and off. It refreshes many times in a second -
and if 50% of the times it is on, then the pixel appears at 50% the brightness). Color is achieved
by a using a color wheel between the light source and the mirrors - this splits the light in
red/green/blue, and each mirror controls all thee light beams for its designated pixel. So with the
help of tiny projector we will display required menu bar on our arm.

3.2 Bio-Acoustics

Acoustics is the interdisciplinary science that deals with the study of all mechanical waves in
gases, liquids, and solids including vibration, sound, ultrasound and infrasound. A scientist who
works in the field of acoustics is an acoustician while someone working in the field of acoustics
technology may be called an acoustical engineer. The application of acoustics can be seen in
almost all aspects of modern society with the most obvious being the audio and noise control
industries. Bioacoustics is a cross-disciplinary science that combines biology and acoustics.
Usually it refers to the investigation of sound production, dispersion through elastic media, and
reception in animals, including humans.
When a finger taps the skin, several distinct forms of acoustic energy are produced. Some energy
is radiated into the air as sound waves; this energy is not captured by the Skinput system. 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. When shot with a high-speed
camera, these appear as ripples, which propagate outward from the point of contact. 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.g., wrist, palm,
fingers), which have negligible compliance.
In addition to the energy that propagates on the surface of the arm, some energy is transmitted
inward, toward the skeleton. These longitudinal (compressive) waves travel through the soft
tissues of the arm, exciting the bone, which is much less deformable then the soft tissue but can
respond to mechanical excitation by rotating and translating as a rigid body. This excitation

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vibrates soft tissues surrounding the entire length of the bone, resulting in new longitudinal
waves that propagate outward to the skin.

3.2.1 Transverse Wave Propagation :

Figure 3: Finger impacts displace the skin, creating transverse waves (ripples). The sensor is
activated as the wave passes underneath it.

3.2.2 Longitudinal Wave Propagation :

Figure 4: Finger impacts create longitudinal (compressive) waves that cause internal skeletal
structures to vibrate. This, in turn, creates longitudinal waves that emanate outwards from the
bone (along its entire length) toward the skin.

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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.
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.
While we do not explicitly model the specific mechanisms of conduction, or depend on these
mechanisms for our analysis, we do believe the success of our technique depends on the complex
acoustic patterns that result from mixtures of these modalities. Similarly, we also believe that
joints play an important role in making tapped locations acoustically distinct. Bones are held
together by ligaments, and joints often include additional biological structures such as fluid
cavities. This makes joints behave as acoustic filters. In some cases, these may simply dampen
acoustics; in other cases, these will selectively attenuate specific frequencies, creating location
specific acoustic signatures.

Figure 5: Arm band which consists of vibration sensor array

3.2.3 Bioacoustic Sensor:

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The Minisense 100 is a low-cost cantilever-type vibration sensor loaded by a mass to offer high
sensitivity at low frequencies. The pins are designed for easy installation and are solderable.
Horizontal and vertical mounting options are offered as well as a reduced height version. The
active sensor area is shielded for improved RFI/EMI rejection. Rugged, flexible PVDF sensing
element withstands high shock overload. Sensor has excellent linearity and dynamic range, and
may be used for detecting either continuous vibration or impacts.
Some features of Minisense 100 are given below:
High Voltage Sensitivity (1 V/g)
Over 5 V/g at Resonance
Horizontal or Vertical Mounting
Shielded Construction
Solderable Pins, PCB Mounting
Low Cost
< 1% Linearity
Up to 40 Hz (2,400 rpm) Operation Below Resonance

3.3 Bluetooth ;

Bluetooth is a wireless technology standard for exchanging data over short distances (using
short-wavelength radio transmissions in the ISM band from 2400–2480 MHz) from fixed and
mobile devices, creating personal area networks (PANs) with high levels of security. Created by
telecoms vendor Ericsson in 1994, it was originally conceived as a wireless alternative to RS-232
data cables. It can connect several devices, overcoming problems of synchronization. Bluetooth
takes small-area networking to the next level by removing the need for user intervention and
keeping transmission power extremely low to save battery power.

Bluetooth is essentially a networking standard that works at two levels:

It provides agreement at the physical level -- Bluetooth is a radio-frequency standard.

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It provides agreement at the protocol level, where products have to agree on when bits are
sent, how many will be sent at a time, and how the parties in a conversation can be sure
that the message received is the same as the message sent.

Bluetooth is intended to get around the problems that come with infrared systems. The older
Bluetooth 1.0 standard has a maximum transfer speed of 1 megabit per second (Mbps), while
Bluetooth 2.0 can manage up to 3 Mbps. Bluetooth 2.0 is backward-compatible with 1.0 devices.

One of the ways Bluetooth devices avoid interfering with other systems is by sending out very
weak signals of about 1 milliwatt. By comparison, the most powerful cell phones can transmit a
signal of 3 watts. The low power limits the range of a Bluetooth device to about 10 meters (32
feet), cutting the chances of interference between your computer system and your portable
telephone or television. Even with the low power, Bluetooth doesn't require line of sight between
communicating devices. The walls in your house won't stop a Bluetooth signal, making the
standard useful for controlling several devices in different rooms.

Bluetooth can connect up to eight devices simultaneously. With all of those devices in the same
10-meter (32-foot) radius, you might think they'd interfere with one another, but it's unlikely.
Bluetooth uses a technique called spread-spectrum frequency hopping that makes it rare for more
than one device to be transmitting on the same frequency at the same time. In this technique, a
device will use 79 individual, randomly chosen frequencies within a designated range, changing
from one to another on a regular basis. In the case of Bluetooth, the transmitters change
frequencies 1,600 times every second, meaning that more devices can make full use of a limited
slice of the radio spectrum. Since every Bluetooth transmitter uses spread-spectrum transmitting
automatically, it’s unlikely that two transmitters will be on the same frequency at the same time.
This same technique minimizes the risk that portable phones or baby monitors will disrupt
Bluetooth devices, since any interference on a particular frequency will last only a tiny fraction
of a second.

So we are connecting armband and mobile device using Bluetooth technology. So whatever data
is received by the sensors are transferred to the mobile device. That mobile device samples the
data and compared it with the stored data and according to the algorithm task is performed.

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CHAPTER 4

Experiments
4.1 Experimental Conditions :

To evaluate the performance of our system, they recruited 13 participants (7 female) from the
Greater Seattle area. These participants represented a diverse crosssection of potential ages and
body types. Ages ranged from 20 to 56 (mean 38.3), and computed body mass indexes (BMIs)
ranged from 20.5 (normal) to 31.9 (obese).
We selected three input groupings from the multitude of possible location combinations to test.
We believe that these groupings, illustrated in Figure 7, are of particular interest with respect to
interface design, and at the same time, push the limits of our sensing capability. From these three
groupings, we derived five different experimental conditions, described below. Fingers (Five
Locations) one set of gestures we tested had participants tapping on the tips of each of their five
fingers (Figure ―Fingers‖). The fingers offer interesting affordances that make them compelling
to appropriate for input. Foremost, they provide clearly discrete interaction points, which are
even already well-named (e.g., ring finger). In addition to five finger tips, there are 14 knuckles
(five major, nine minor), which, taken together, could offer 19 readily identifiable input locations
on the fingers alone. Second, we have exceptional finger to finger dexterity, as demonstrated
when we count by tapping on our fingers. Finally, the fingers are linearly ordered, which is
potentially useful for interfaces like number entry, magnitude control (e.g., volume), and menu
selection. At the same time, fingers are among the most uniform appendages on the body, with
all but the thumb sharing a similar skeletal and muscular structure. This drastically reduces

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acoustic variation and makes differentiating among them difficult. Additionally, acoustic
information must cross as many as five (finger and wrist) joints to reach the forearm, which
further dampens signals. For this experimental condition, we thus decided to place the sensor
arrays on the forearm, just below the elbow. Despite these difficulties, pilot experiments showed
measureable acoustic differences among fingers, which we theorize is primarily related to finger
length and thickness, interactions with the complex structure of the wrist bones, and variations in
the acoustic transmission properties of the muscles extending from the fingers to the forearm.
Whole Arm (Five Locations) Another gesture set investigated the use of five input locations On
the forearm and hand: arm, wrist, palm, thumb and middle finger (Figure, ―Whole Arm‖). We
selected these locations for two important reasons. First, they are distinct and named parts of the
body (e.g., ―wrist‖). This allowed participants to accurately tap these locations without training
or markings. Additionally, these locations proved to be acoustically distinct during piloting, with
the large spatial spread of input points offering further variation. We used these locations in three
different conditions.
One condition placed the sensor above the elbow, while another placed it below. This was
incorporated into the experiment to measure the accuracy loss across this significant articulation
point (the elbow). Additionally, participants repeated the lower placement condition in an eyes-
free context: participants were told to close their eyes and face forward, both for training and
testing. This condition was included to gauge how well users could target on-body input
locations in an eyes-free context (e.g., driving). Forearm (Ten Locations) In an effort to assess
the upper bound of our approach’s sensing resolution, our fifth and final experimental condition
used ten locations on just the forearm (Figure 6, ―Forearm‖). Not only was this a very high
density of input locations (unlike the whole-arm condition), but it also relied on an input surface
(the forearm) with a high degree of physical uniformity (unlike, e.g., the hand). We expected that
these factors would make acoustic sensing difficult. Moreover, this location was compelling due
to its large and flat surface area, as well as its immediate accessibility, both visually and for
finger input. Simultaneously, this makes for an ideal projection surface for dynamic interfaces.
To maximize the surface area for input, we placed the sensor above the elbow, leaving the entire
forearm free. Rather than naming the input locations, as was done in the previously described
conditions, we employed small, colored stickers to mark input targets. This was both to reduce
confusion (since locations on the forearm do not have common names) and to increase input

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consistency. As mentioned previously, we believe the forearm is ideal for projected interface
elements; the stickers served as low tech placeholders for projected.

Design and Setup :

We employed a within-subjects design, with each participant performing tasks in each of the five
conditions in randomized order: five fingers with sensors below elbow; five points on the whole
arm with the sensors above the elbow; the same points with sensors below the elbow, both
sighted and blind; and ten marked points on the forearm with the sensors above the elbow.
Participants were seated in a conventional office chair, in front of a desktop computer that
presented stimuli. For conditions with sensors below the elbow, we placed the armband~3cm
away from the elbow, with one sensor package near the radius and the other near the ulna. For
conditions with the sensors above the elbow, we placed the armband ~7cm above the elbow,
such that one sensor package rested on the biceps. Right-handed participants had the armband
placed on the left arm, which allowed them to use their dominant hand for finger input. For the
one left-handed participant, we flipped the setup, which had no apparent effect on the operation
of the system. Tightness of the armband was adjusted to be firm, but comfortable. While
performing tasks, participants could place their elbow on the desk, tucked against their body, or
on the chair’s adjustable armrest; most chose the latter.

Procedure :

For each condition, the experimenter walked through the input locations to be tested and
demonstrated finger taps on each. Participants practiced duplicating these motions for
approximately one minute with each gesture set. This allowed participants to familiarize
themselves with our naming conventions (e.g. ―pinky‖, ―wrist‖), and to practice tapping their
arm and hands with a finger on the opposite hand. It also allowed us to convey the appropriate
tap force to participants, who often initially tapped unnecessarily hard. To train the system,
participants were instructed to comfortably tap each location ten times, with a finger of their
choosing. This constituted one training round. In total, three rounds of training data were
collected per input location set (30 examples per location, 150 data points total). An exception to
this procedure was in the case of the ten forearm locations, where only two rounds were

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collected to save time (20 examples per location, 200 data points total). Total training time for
each experimental condition was approximately three minutes. We used the training data to build
an SVM classifier. During the subsequent testing phase, we presented participants with simple
text stimuli (e.g. ―tap your wrist‖), which instructed them where to tap. The order of stimuli was
randomized, with each location appearing ten times in total. The system performed real-time
segmentation and classification, and provided immediate feedback to the participant (e.g. ―you
tapped your wrist‖). We provided feedback so that participants could see where the system was
making errors (as they would if using a real application).

Figure 6: Accuracy of the three whole-arm-centric conditions. Error bars represent standard
deviation.

If an input was not segmented (i.e. the tap was too quiet), participants could see this and would
simply tap again. Overall, segmentation error rates were negligible in all conditions, and not
included in further analysis. In this section, we report on the classification accuracies for the test
phases in the five different conditions. Overall, classification rates were high, with an average
accuracy across conditions of 87.6%. Additionally, we present preliminary results exploring the
correlation between classification accuracy and factors such as BMI, age, and sex.

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Five Fingers
Despite multiple joint crossings and ~40cm of separation between the input targets and sensors,
classification accuracy remained high for the five-finger condition, averaging 87.7%
(SD=10.0%, chance=20%) across participants. Segmentation, as in other conditions, was
essentially perfect. Inspection of the confusion matrices showed no systematic errors in the
classification, with errors tending to be evenly distributed over the other digits. When
classification was incorrect, the system believed the input to be an adjacent finger 60.5% of the
time; only marginally above prior probability (40%). This suggests there are only limited
acoustic continuities between the fingers. The only potential exception to this was in the case of
the pinky, where the ring finger constituted 63.3% percent of the misclassifications.

Whole Arm
Participants performed three conditions with the whole-arm location configuration. The below-
elbow placement performed the best, posting a 95.5% (SD=5.1%, chance=20%) average
accuracy. This is not surprising, as this condition placed the sensors closer to the input targets
than the other conditions. Moving the sensor above the elbow reduced accuracy to 88.3%
(SD=7.8%, chance=20%), a drop of 7.2%. 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. Figure
8 shows these results. The eyes-free input condition yielded lower accuracies than other
conditions, averaging 85.0% (SD=9.4%, chance=20%). This represents a 10.5% drop from its
vision assisted, but otherwise identical counterpart condition. It was apparent from watching
participants complete this condition that targeting precision was reduced. In sighted conditions,
participants appeared to be able to tap locations with perhaps a 2cm radius of error. Although not
formally captured, this margin of error appeared to double or triple when the eyes were closed.
We believe that additional training data, which better covers the increased input variability,
would remove much of this deficit. We would also caution designers developing eyes-free, on-
body interfaces to carefully consider the locations participants can tap accurately.

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Figure 7: Higher accuracies can be achieved by collapsing the 10 input locations into groups. A–
E and G were designed to be spatially intuitive. F was created following analysis of per-location
accuracy data.

Higher accuracies can be achieved by collapsing the ten input locations into groups. First two
were created using a design-centric strategy. Last figure was created following analysis of per-
location accuracy data.

Forearm
Classification accuracy for the ten-location forearm condition stood at 81.5% (SD=10.5%,
chance=10%), a surprisingly strong result for an input set we devised to push our system’s
sensing limit (K=0.72, considered very strong). Following the experiment, we considered
different ways to improve accuracy by collapsing the ten locations into larger input groupings.
The goal of this exercise was to explore the tradeoff between classification accuracy and number
of input locations on the forearm, which represents a particularly valuable input surface for
application designers. We grouped targets into sets based on what we believed to be logical
spatial groupings. In addition to exploring classification accuracies for layouts that we
considered to be intuitive, we also performed an exhaustive search over all possible groupings.
For most location counts, this search confirmed that our intuitive groupings were optimal;
however, this search revealed one plausible, although irregular, layout with high accuracy at six
input locations (Figure 9, F). Unlike in the five-fingers condition, there appeared to be shared

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acoustic traits that led to a higher likelihood of confusion with adjacent targets than distant ones.
This effect was more prominent laterally than longitudinally. Figure illustrates this with lateral
groupings consistently outperforming similarly arranged, longitudinal groupings (B and C vs. D
and E). This is unsurprising given the morphology of the arm, with a high degree of bilateral
symmetry along the long axis.

4.2 ANALYSIS :

The audio stream was segmented into individual taps using an absolute exponential average of
all sensor channels (Figure, red waveform). When an intensity threshold was exceeded (Figure,
upper blue line), the program recorded the timestamp as a potential start of a tap. If the intensity
did not fall below a second, independent ―closing‖ threshold (Figure, lower purple line) between
100 and 700 ms after the onset crossing (a duration we found to be the common for finger
impacts), the event was discarded. If start and end crossings were detected that satisfied these
criteria, the acoustic data in that period (plus a 60 ms buffer on either end) was considered an
input event (Figure , vertical green regions). Although simple, this heuristic proved to be robust.
After an input has been segmented, the waveforms are analyzed. We employ a brute force
machine learning approach, computing 186 features in total, many of which are derived
combinatorially. For gross information, we include the average amplitude, standard deviation
and total (absolute) energy of the waveforms in each channel (30 features). From these, we
calculate all average amplitude ratios between channel pairs (45 features). We also include an
average of these ratios (1 feature). We calculate a 256-point FFT for all 10 channels, although
only the lower 10 values are used (representing the acoustic power from 0 to 193 Hz), yielding
100 features. These are normalized by the highest amplitude FFT value found on any channel.

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Figure 8: Ten channels of acoustic data generated by three finger taps on the forearm, followed
by three taps on the wrist. The exponential average of the channels is shown in red. Segmented
input windows are highlighted in green. Note how different sensing elements are activated by the
two locations

We also include the center of mass of the power spectrum within the same 0–193 Hz range for
each channel, a rough estimation of the fundamental frequency of the signal displacing each
sensor (10 features). Subsequent feature selection established the all-pairs amplitude ratios and
certain bands of the FFT to be the most predictive features. These 186 features are passed to a
support vector machine (SVM) classifier. A full description of SVMs is beyond the scope of this
paper (see Burges3 for a tutorial). Our software uses the implementation provided in the Weka
machine learning toolkit.26 It should be noted, however, that other, more sophisticated
classification techniques and features could be employed. Thus, the results presented in this
paper should be considered a baseline. Before the SVM can classify input instances, it must first
be trained to the user and the sensor position. This stage requires the collection of several
examples for each input location of interest. When using Skinput to recognize live input, the
same 186 acoustic features are computed on the-fly for each segmented input. These are fed into
the trained SVM for classification. We use an event model in our software—once an input is
classified, an event associated with that location is instantiated. Any interactive features bound to
that event are fired.

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SVM
A support vector machine (SVM) is a concept in statistics and computer science for a set of
related supervised learning methods that analyze data and recognize patterns, used for
classification and regression analysis. The standard SVM takes a set of input data and predicts,
for each given input, which of two possible classes forms the input, making the SVM a non-
probabilistic binary linear classifier. Given a set of training examples, each marked as belonging
to one of two categories, an SVM training algorithm builds a model that assigns new examples
into one category or the other. An SVM model is a representation of the examples as points in
space, mapped so that the examples of the separate categories are divided by a clear gap that is as
wide as possible. New examples are then mapped into that same space and predicted to belong to
a category based on which side of the gap they fall on.

4.3 BMI EFFECT:

Early on, we suspected that our acoustic approach was susceptible to variations in body
composition. This included, most notably, the prevalence of fatty tissues and the density/ mass of
bones. These, respectively, tend to dampen or facilitate the transmission of acoustic energy in the
body. To assess how these variations affected our sensing accuracy, we calculated each
participant’s body mass index (BMI) from self-reported weight and height. Data & observations
from the experiment suggest that high BMI is correlated with decreased accuracies.

Figure 9: Accuracy was significantly lower for participants with BMIs above the 50th percentile.

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The participants with the three highest BMIs (29.2, 29.6, and 31.9 – representing borderline
produced the three lowest average accuracies. Figure illustrates this significant disparity here
participants are separated into two groups, those with BMI greater and less than the US national
median, age and sex adjusted [5] (F1,12=8.65, p=.013). Other factors such as age and sex, which
may be correlated to BMI in specific populations, might also exhibit a correlation with
classification accuracy. For example, in our participant pool, males yielded higher classification
accuracies than females, but we expect that this is an artifact of BMI correlation in our sample,
and probably not an effect of sex directly.

22


CHAPTER 5

Advantages

Easy to work: Skinput technology is very easy to understand and it’s very easy to use, it
takes only 20 mins to figure out how to work it.
No interaction with the gadget : If we have to use any application of our mobile then we
reach to our pocket take out the device, unlock it and then go to the application. By using
Skinput we do not need any interaction with the gadget. We have to just tap our finger
and the desired function will performed by the system.
No worry about keypad : People with large fingers gets trouble while operating touch
screens. Using Skinput we get very large interaction surface area. So for such people this
problem will resolve.
Without Visual Contact : For some operations like music players we need only 4-5
buttons. So we can use each fingertip as a button. For such operation we don’t any
display. We can operate such functions without any visual contact.
Easy to access when your phone is not available.
Allows users to interact more personally with their device.
Larger buttons to reduce the risk of pressing the wrong buttons.
Through the use of a sense called proprioception after user learns where the locations are
on the skin they will no longer have to look down to use Skinput reducing people looking
down at their phone while driving.
It can be used for a more interactive gaming experience.

23


CHAPTER 6

Disadvantages

Skinput has its downfalls, especially due to fact of the BIG band that looks very easy to
put on. Many people would not wear a very big band around their arm for the day just to
have this product.
Everybody can’t use this product, the elderly for example have a hard time adapting to
technology as it is. We also have to take in consideration the inconvenience it would
cause to people with invisible disabilities.
This technology only works on direct skin exposure. We cannot use full sleeves shirt
when we are using this technology.
Currently there are only five buttons with accuracy more than 95%. A phone uses at least
10 buttons to dial a phone number or send a text message. So in such cases it will create
problem.
The easy accessibility will cause people to be more socially distracted.
If the user has more than a 30% Body Mass Index, Skinput is reduced to 80% accuracy.
The arm band is currently bulky.
The visibility of the projection of the buttons on the skin can be reduced if the user has a
tattoo located on their arm

24


CHAPTER 7
Applications
We can use Skinput technology in any mobile device. We just need different software for
different mobiles like for mobiles which supports android operating system requires android
application or Symbian operating system requires .jar or .sis software.
We can use this technology in i-pods or other music devices which supports Bluetooth
technology. For such music devices we just need 4 or 5 different buttons. So we can use our
fingertips as input. Like this we can operate these devices without any visual contact.
In gaming devices we can use this technology. So without any joysticks or touch screens we can
play games very easily.
Person with physical disabilities can operate this system very easily.
For simpler browsing system which require less number of buttons (maximum 10) can be
replaced by this technology.

25


CHAPTER 8

FUTURE IMPLEMENTATION
In order to assess the real-world practicality of Skinput, we are currently building a successor to
our prototype that will incorporate several additional sensors, particularly electrical sensors and
inertial sensors (accelerometers and gyroscopes). In addition to expanding the gesture vocabulary
beyond taps, we expect this sensor fusion to allow considerably more accuracy—and more
robustness to false positives—than each sensor alone. This revision of our prototype will also
allow us to benefit from anecdotal lessons learned since building our first prototype: in
particular, early experiments with subsequent prototypes suggest that the hardware filtering we
describe above can be effectively replicated in software, allowing us to replace our relatively
large piezoelectric sensors with micro-machined accelerometers.

This considerably reduces the size and electrical complexity of our armband. Furthermore,
anecdotal evidence has also suggested that vibration frequency ranges as high as several
kilohertz may contribute to tap classification, further motivating the use of broadband
accelerometers. Finally, our multi-sensor armband will be wireless, allowing us to explore a wide
variety of usage scenarios, as well as our general assertion that always-available input will
inspire radically new computing paradigms.

26


Conclusion
In this paper, we have presented our approach to appropriating the human body as an input
surface. We have described 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. Results from
experiments have shown that the system performs very well for a series of gestures, even when
the body is in motion. Additionally, we have presented initial results demonstrating other
potential uses of our approach, which we hope to further explore in future work. These include
single-handed gestures, taps with different parts of the finger, and differentiating between
materials and objects. We conclude with descriptions of several prototype applications that
demonstrate the rich design space we believe Skinput enables.

27


REFERENCES
1) Chris Harrison, Desney Tan, and Dan Morris ―Skinput: Appropriating the Skin as an
Interactive Canvas‖ Microsoft Research 2011.
2) Chris Harrison, Scott E. Hudson ―Scratch Input: Creating, Large Inexpensive,
Unpowered and Mobile Finger Input Surfaces‖UIST 2008.
3) Amento, B.Hill, W.Terveen ―The Sound of one Hand: A wrist- mounted bio-acoustic
fingertip gesture interface‖ CHI’02.
4) Thomas Hahn ―Future Human Computer Interaction with special focus on input and
output techniques‖ HCI March 2006.
5) Burges, C.J. A Tutorial on Support Vector Machines for Pattern Recognition. Data
Mining and Knowledge Discovery, 2.2, June 1998, 121-167.
6) Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and
Obesity in Adults. National Heart, Lung and Blood Institute. Jun. 17, 1998.
7) Deyle, T., Palinko, S., Poole, E.S., and Starner, T. Hambone: A Bio-Acoustic Gesture
Interface. In Proc. ISWC '07. 1-8.
8) Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., and Twombly, X. Vision-based hand
pose estimation: A review. Computer Vision and Image Understanding. 108, Oct., 2007.
9) Fabiani, G.E. McFarland, D.J. Wolpaw, J.R. and Pfurtscheller, G. Conversion of EEG
activity into cursor movement by a brain-computer interface (BCI). IEEE Trans. on
Neural Systems and Rehabilitation Engineering, 12.3, 331-8. Sept. 2004.
10) Grimes, D., Tan, D., Hudson, S.E., Shenoy, P., and Rao, R. Feasibility and pragmatics
of classifying working memory load with an electroencephalograph. Proc. CHI ’08, 835-
844.
11) Harrison, C., and Hudson, S.E. Scratch Input: Creating Large, Inexpensive, Unpowered
and Mobile finger Input Surfaces. In Proc. UIST ’08, 205-208.

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