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user being monitored. For example, while monitoring the heart rate
of the patient, thresholds for what constitute a normal heart rate are
adjusted by inferring the activity of the patient (e.g., sitting down vs.
walking). The battery power of the sensors are extended with analytical
rules requiring sensors to report measurement in a more granular way
in emergencies or during abnormal physiological episodes [70].
Several Wireless Body Sensor Networks (BSNs) [73] [74] have been
used in several pilot applications for monitoring elderly patients with
chronic conditions in out-patient settings. Using medical sensors such
as ECG, several cardiovascular related illnesses can be detected as early
as possible by simply monitoring heart-beat rhythm (arrhythmias). Mul-
tiple heterogeneous sensor architecture can help expending the bound-
aries of BSNs application ranges. The DexterNet BSNs [75] use motion
sensors (motes), GPS, and airborne particulate matter (PM) sensors
to monitor as well as prevent asthma. Motion sensors (Accelerometer)
combined with with Electromyogram (EMG) sensors that capture hu-
man motion balancing and muscular actives have been used to build
postural stability and subject-independent classification models.
Recent work on using motion sensors with other body sensors is in-
cluded in the myHealthAssistant [76] project. The focus of this project is
on preventive health care and development of a system that helps reduc-
ing physical inactivity. The system captures individual activity through
the day, and motivates users by calculating a new workout plan based
on completed workouts 3 . In the base setup for daily activity monitoring,
a set of a single customized accelerometer, a smartphone, and a heart
rate sensor are used to identify five different activities, monitor the heart
rate and calculate the calorie expenditure. The system also allows the
user to wear two additional accelerometers (strapped around the torso,
and attached to the right weight lifting glove) while exercising in order
to get a more accurate identification of 16 activities, and calorific expen-
diture. The analysis is performed at a local computer using a Gaussian
model-based classifier.
Many other body sensor applications have been designed to monitory
physical activity 4 as it is critical to maintain physical and psychologi-
cal health, and reduce the risk of premature mortality, coronary heart
3 Several studies [77] have shown that internet and phone based user motivation systems
can significantly increase the level of physical activity.
4 Commercial systems to encourage physical activity are used only while performing the
target activity and are not trying to disambiguate that activity. Such technologies include
Dance Dance Revolution, the Nintendo Wii Fit, the Nike+ system, Garmins Forerunner,
Bones in Motions Active Mobile and Active Online, bike computers, heart rate monitors,
MPTrain [17], Jogging over a distance [15], and mixed- and virtualreality sports games [13,14].
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