Smartphones can obtain information about its owner, and many
researchers are dedicated to finding ways to gather and interpret the most
useful information. Modern smartphones are packed with many powerful sensors
that enable the phone to collect data about you. Although that may alarm anyone
who is concerned about privacy, the sensors also present an opportunity to help
smartphone users in previously impossible ways. The WISDM (Wireless Sensor Data
Mining) Lab led by Dr. Gary Weiss is concerned with collecting the sensor data
from smart phones and other mobile devices and mining sensor data for useful
knowledge. Smartphones contain more sensors than most people would ever
imagine. Android phones and iPhones include an audio sensor (microphone), image
sensor (camera), touch sensor (screen), acceleration sensor (tri-axial
accelerometer), light sensor, proximity sensor, and several sensors (including
the Global Positioning System) for establishing location.
Their first goal was to use the
accelerometer to perform activity recognition -- to identify the physical
activity, such as walking, that a smartphone user is performing which could be
used as the basis for many health and fitness applications, and could also be
used to make the smartphone more context-sensitive, so that its behavior would
take into account what the user is doing. The phone could then, for example,
automatically send phone calls to voice mail if the user was jogging. They have
used existing classification algorithms to identify activities, such as
walking, and help map accelerometer data to those activities. Also, they have
also found that one's gait, as measured by a smartphone accelerometer, is
distinctive enough to be used for identification purposes from a pool of
several hundred smartphone users with 100 percent accuracy using previous data
sample. This application is important since gait problems are often indicators
of other health problems. All of these applications are based on the same
underlying methods of classification as our activity recognition work.
They have collected a small
amount of labeled "training" data from a panel of volunteers for each
of these activities such as walking, jogging, climbing stairs, sitting,
standing, and lying down, with the expectation that the model that system generates
will be applicable to other users. Initially, the system could identify the six
activities listed above with about 75 percent accuracy. These results are
adequate for obtaining a general picture of how much time a person spends on
each activity daily, but are far from ideal. However, if a small amount of data
can be obtained that a user actively labels as being connected with a
particular activity, they will be able to build a personal model for that user,
with accuracy in the 98-99 percent range. This shows that people move
differently and that these differences are important when identifying
activities. A system Actitracker allows you to review reports of your
activities via a web-based user interface. This will determine how active or how
inactive you are. These reports may serve as a wakeup call to some and hope it
will lead to positive changes in behavior. Such a tool could also be used by a
parent to monitor the activities of their child, and thus could even help
combat conditions such as childhood obesity.
This category of applications
is part of a growing trend towards mobile health. As new sensors become
available and as existing sensors are improved, even more powerful
smartphone-based health applications should appear. For example, other
researchers are boosting the magnification of smartphone cameras so that they
can analyze blood and skin samples. Researchers at MIT's Mobile Experience Lab
are even developing a sensor that attaches to clothing, which will allow
smartphones to track their users' exposure to ultraviolet radiation and the
potential for sunburn. Smartphone sensor technology, especially when combined
with data mining, offers tremendous opportunities for new and innovative
applications. Looking at this applications, it is estimated that there will be
a flood of new sensor-based apps over the next decade.
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