In
science fiction films from Aliens to Avatar, commanders back at the base station always know when soldiers of the
future get taken out by hostiles–because their vital signs are being
monitored in real time. Doing that with present-day technology is a challenge,
not least because collecting and transmitting all of the data that can be
gathered by even a handful of motion and vital-signs sensors would be a huge
drain on battery power and wireless bandwidth.
By
equipping the clothing and bodies of users with a mesh of multiple sensors
– known as “smart dust” – that report to an Android-powered
phone, researchers are pioneering an open-source route to realizing the dream
of always-on medical monitoring. Their work has already allowed them to measure
how much test subjects exercise, how well their hearts are doing and how much
air pollution they’re being exposed to.
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The
resulting data have a number of applications:
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Incorporation of historical and real-time data on vital signs into permanent
medical records
Automatically inform a patient when to adjust their heart medication
Turn exercise and daily activity levels into a Foursquare-style competition
Allow users to avoid locations and times of day when air pollution is worst
The technology (pdf) is described in a paper
to be delivered in late June at the 2010 International conference on Pervasive technologies for
assistive environment in Samos, Greece. It outlines a hierarchy of
processing steps that make 24/7 monitoring of vital signs (such as breathing
and heart rate) realistic given the battery life issues and bandwidth
constraints of mobile phones
Three-layer architecture of the DexterNet system with example hardware, communication and software implementation.
This
hierarchy, known as DexterNet, includes sequential processing at each level of
the hardware involved: the sensors, known as the body sensor layer, the smartphone or personal network layer, and finally in the “cloud” or global network layer that backs up and
does final processing of all of the user’s data. The purpose of in-device
processing in each layer is the reduction of the amount of information
transmitted wirelessly between each device.
The
lowest level of this hierarchy, individual sensors on the user’s limbs and
torso, can gather data on a number of parameters: motion in 3 axes (realized
with a three-axis accelerometer and a two-axis gyroscope), heart ECG, levels of
airborne particulate matter, and, for breathing movements, “electrical impedance pneumography.”
To
reduce the frequency with which these sensors must communicate with the user’s
smartphone (and the volume of information they have to transmit) these sensors
are capable of basic signal-processing algorithms across a programmer-definable
time period, including minimum, maximum, average and mean values for any
particular parameter.
Two
types of sensors were used, one, known as the TelosB, is about the size of a USB thumb
drive, and sports a Texas Instruments processor often found in embedded
applications and 10k of integrated RAM. The other, Intel’s
SHIMMER sensor, runs the TinyOS operating system designed specifically
for remote sensors, weighs only 15 grams and is not much bigger than a quarter.
Led
by Edmund Seto of the School of Public Health at UC Berkeley, the researchers
involved were able to further integrate data gathered from the wireless sensors
with data gathered by the phones themselves. By combining location, time of day
and air-quality data, for example, the researchers were able to create maps of
user’s days that highlight the places and times when they were exposed to greatest
levels of air pollution.
Because
phones and sensors can communicate with each other wirelessly via Bluetooth,
the number of sensors that can be embedded both on a user and in his or her
environment is practically limitless. In one application, the researchers put a
sensor into the digital bathroom scale of users and their blood pressure
monitors to quantify daily changes related to too much fluid retention in
patients. The resulting data allowed their algorithms, processed in by the
server to which the smartphone sends its data, to suggest possible modification
of dosage of blood pressure medication.
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Seto
et al. cited the Android platform as a unique enabler of their work, not only
because Android phones, like all smart phones, are fairly capable wearable
computers in their own right. Because Android is open-source, the researchers
were able to develop on top of it using the SPINE platform for remote sensing,
and to add to it their own API, known as WAVE (not to be confused with Google’s
Wave). In combination, these research platforms allow them free reign to
experiment.
So
far the only drawback to using the Android platform in this work, note the
researchers, is that it can’t locate users indoors. The researchers spend a
portion of their paper trying to re-invent the wheel by speculating about ways
to accomplish this via the use of Wifi nodes and even visual recognition of
interior spaces using the phone’s camera, without ever realizing, apparently,
that Skyhook Wireless already has an API and an
international database of wifi networks that can accomplish this.
