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From: Qasim Sheikh
To: Dr. Asad Naeem ; Abdullah Sadiq ; Shiraz Shahid ; Fakhar Lodhi ; Qasim Sheikh
Sent: Monday, June 3, 2013 3:56 PM
Subject: This article is a treasure of ideas for FYP

Microphones as sensors
Teaching old microphones new tricks
Sensor technology: Microphones are designed to capture sound. But they turn out to
be able to capture other sorts of information, too
Jun 1st 2013 | From the print edition
MICROPHONES exist in many shapes and sizes,
and work in many different ways. In the late 19th
century, early telephones relied on carbon
microphones, pioneered by Thomas Edison;
today's smartphones contain tiny microphones
based on micro-electro-mechanical systems,
commonly called MEMS. Specialist microphones

abound in recording studios; others are used by
spies. But whatever the technology, these microphones all do the same thing: they convert
sound waves into an electrical signal.
It turns out, however, that with the addition of suitable software, microphones can detect more

than mere audio signals. They can act as versatile sensors, capable of tuning into signals from
inside the body, assessing the social environment and even tracking people's posture and
gestures. Researchers have reimagined microphones as multi-talented collectors of information.

And because they are built into smartphones that can be taken anywhere, and can acquire new
abilities simply by downloading an app, they are being put to a range of unusual and beneficial
uses.

That natural microphone, the human ear, is finely attuned to picking up certain characteristics
in a person's voice. It is not hard, for instance, to infer from a slight change in pitch when a
friend might be under stress. Tanzeem Choudhury of Cornell University and her research team

are building mobile-phone software that can be trained to do the same thing. That stress resultsin subtle changes in pitch, amplitude and frequency, as well as speaking rate, has been known
for decades. But humans respond to stress in different ways and have different coping styles,

says Dr Choudhury. So a one-size-fits-all smartphone app which analyses speech will not
provide an accurate assessment of whether someone is stressed or not.
The sound of stress
Dr Choudhury's solution is an app called StressSense. Running on a standard Android-based

smartphone, it listens for certain universal indicators of stress but is able, over time, to learn the
specifics of a particular user's voice. It is unobtrusive yet is also robust enough to work in noisy

environments, which is crucial if it is to be of practical diagnostic use. StressSense does not
actually record speech, Dr Choudhury emphasises, but simply captures and analyses
characteristics such as amplitude and frequency. In a paper published last year, the researchers

concluded that "it is feasible to implement a computationally demanding stress-classification
system on off-the-shelf smartphones". Their ultimate goal is to develop an app that can help
someone determine the links between irritating situations and subsequent responses. Your

phone might realise before you do, for example, that your 8am meetings are the cause of your
headaches.
StressSense is still in development. In the meantime, Dr Choudhury's team has launched an

Android app called BeWell that focuses more on overall health by looking at three metrics:
sleep, physical activity and social interaction. These three metrics, Dr Choudhury believes, are
important yet easily measured indicators of someone's health. BeWell's sleep-tracking feature

guesses whether the phone's user is awake or not by analysing usage, light and sound levels,
and charging habits. Physical activity is monitored using built-in accelerometers for motiondetection. And social activity is measured chiefly by collecting snippets of sound that indicate

that the user is talking to someone, either in person or over the phone.
Again, no actual words are stored, simply features of human speech, which the app can
distinguish from background noise such as music or traffic. Certain changes in a person's social

interactions—a sudden drop-off, for instance—can be indicative of health problems such as
depression, says Dr Choudhury. The idea is that the app could tip someone off to a change in
their behaviour that might otherwise have gone unnoticed.

Microphones need not limit themselves to listening to the human voice, however. John
Stankovic of the University of Virginia in Charlottesville is using microphones to capture
heartbeats. Researchers in his group are using earphones modified with accelerometers and

additional microphones that detect the pulse in arteries in the wearer's ear. This makes it
possible to collect information about the wearer's physical state, including heart rate and
activity level, which is transmitted to the smartphone via the audio jack. The researchers evencreated an app, called MusicalHeart, that analyses the wearer's heart rate and recommends

songs from a music library based on a heart-rate goal—faster to encourage a runner, or slower
to calm someone who is feeling nervous.
Dr Stankovic says he is working with
anaesthesiologists to develop a similar system that

could use the calming effect of music to help
people who are about to undergo surgery. The
aim, he says, is to explore the potential for using a
pulse-music feedback system to calm the heart,

rather than simply resorting to drugs to do the
job. In a separate project, Dr Stankovic is
developing sound libraries that can be used to
identify and classify certain types of sounds, from

direct indicators of depression in the human voice
to coughs, wheezes and lung function. Just as a
phone can perform speech recognition, it might
also be able to distinguish between healthy and

unhealthy lung sounds. Dr Stankovic and his
team published details of their proposed
"physiological acoustic anomaly detection service" in a paper in April.
Working in a similar vein, Shwetak Patel, a researcher at the University of Washington in

Seattle, has found a way to use a smartphone to measure lung function from a hearty blow on
its microphone. He and his team have developed an iPhone app, called SpiroSmart, that
simulates a digital spirometer, the device that measures the volume of air a person can expel

from her lungs. Spirometers help doctors better understand the health status of patients with
conditions like asthma, chronic obstructive pulmonary disease (COPD) and cystic fibrosis. But
clinical spirometers are expensive, costing thousands of dollars, which makes them impractical

for widespread home use. Dr Patel and his collaborators reckon that if people who need one
could have a spirometer on their phones, they could better manage their conditions, and their
doctors could spot problems earlier, in between check-ups.

But in deciding to use the microphone on an iPhone, Dr Patel had to rethink the design of a
spirometer, which uses small turbines to measure airflow. Instead of building a turbine add-on
to the phone, his team developed software that listens for acoustic features, such as resonance,

that result from air being expelled through the trachea and past the vocal cords. These features,
he says, directly indicate the volume of air moved from the lungs. In a paper published last
year, 52 patients compared the mobile-based spirometer with a clinical one. SpiroSmart's resultswere accurate to within 5% of those from the gold-standard clinical device. The app is

undergoing clinical trials with patients diagnosed with COPD, cystic fibrosis and chronic
asthma, and Dr Patel hopes to get Federal Drug Administration approval for SpiroSmart as a
medical device by the end of the year.

Another of Dr Patel's microphone-based projects uses sound to recognise hand-movements in
the air, just as touchscreens can distinguish between different gestures. The software, called
SoundWave, produces an inaudible, high-pitched tone from a loudspeaker that bounces off a

nearby object, such as a hand. The microphone picks up the reflected sound, and audioprocessing software can then detect different movements, such as waving your hand, wiggling
your fingers, or making two-handed gestures. It does this by measuring the slight shift in

frequency that results when sound bounces off a moving object, known as the Doppler effect (a
familiar everyday example of which is the change in the pitch of an ambulance's siren as it
passes by). The advantage of this approach is that it allows existing devices (smartphones and

laptops, say) to detect gestures, such as flicking your hand upwards to scroll through a
document, without the need for any additional hardware.
Keeping an ear on drivers
Expanding on this idea, Dr Patel is currently

developing software that uses a smartphone's
existing microphone and speakers and enables the
device to detect its position in a car. Just like
SoundWave, it would produce inaudible tones

that reflect off the car's interior. It might then be
possible to begin and end calls using gestures—or
could, Dr Patel suggests, form the basis of an optin service that locks the phone from the driver,

keeping him from texting or making calls while
driving.
One drawback of using microphones for these
various kinds of sensing is that keeping a
microphone listening at all times, and running

software to analyse what it hears, can consume a
lot of battery power. But a new trick devised by
Qualcomm, a maker of chips for mobile devices, may be able to help. Its Snapdragon 800
processors have a feature called Snapdragon Voice Activation that can wake up a gadget from

standby mode at the sound of a voice command. When no voices can be heard, the deviceremains in a battery-sipping slumber. As smartphones continue to be put to unexpected uses,
the humble microphone is ready to play its part.

--
With Regards

Qasim
pk.linkedin.com/pub/qasim-sheikh/0/250/712
+923008540838 (mob)