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monitors the physical activities of an individual by
wearing a portable device equipped with inertial
sensors. This device aims at improving a person's
awareness and lifestyle by providing on-demand
feedback regarding the user's physical perfor-
mance metrics such as activity type, duration,
and amount of energy expended, with the goal
of motivating physical fitness or active lifestyle
without interfering with the day-to-day activities.
Jovanov, et al. (2005) proposed a miniature
wearable wireless Body Area Network (BAN) to
assess the effectiveness of rehabilitation proce-
dures that were previously limited to laboratory
settings but now can be moved to ambulatory
settings such as stroke rehabilitation, physical
rehabilitation after hip or knee surgeries, myo-
cardial infarction rehabilitation, and traumatic
brain injury rehabilitation. This wireless BAN
is composed of activity sensors: a heart sensor
that can be used to monitor heart activity and
position of the upper trunk; the same sensor can
be used to monitor position and activity of upper
and lower extremities. The sensors would also
allow one to assess metabolic rate and cumula-
tive energy expenditure as a valuable parameter
in the management of many medical conditions.
The wireless BAN can include a number of
physiological sensors depending on the end-user
application (ECG, EMG, EEE, blood pressure
monitor, breathing sensor, movement sensor).
The wireless network nodes can be implemented
as tiny patches or incorporated into clothes or
shoes. The network nodes continuously gather
and process raw information, store them locally,
and send them to the personal server. The type and
nature of a healthcare application will determine
the frequency of relevant events (sampling, pro-
cessing, storing, and communicating).
A different type of wireless portable device was
presented by Yadollahi and Moussawi (2009). The
system proposed detects and monitors Acoustic
Obstructive Sleep Apnea; it requires two data
channels: the tracheal breathing sounds and
the blood oxygenation saturation level. A fully
automated method was developed that uses the
energy of breathing sounds signals to segment the
signals into sound and silent segments. Then, the
sound segments are classified into breath, snore,
and noise segments. The SaO2 signal is analyzed
to find the rises and drops in the SaO2 signal.
Finally a fuzzy algorithm was developed to use
this information and detect apnea and hypopnea
events. The method was evaluated on 40 patients
simultaneously with full night polysomnography
study, and the results were compared with those
of the polysomnography. The results show high
correlation (96%) between the new model system
and polysomnography. Also, the method has been
found to have sensitivity and specificity values of
more than 90% in differentiating simple snorers
from Obstructive Sleep Apnea patients.
Shen, et al. (2008) proposed an ultra-wearable
smart sensor system which combines an ear-lead
electrocardiogram, a tri-axial accelerometer, and a
GPS sensor to measure a normal or elderly person's
daily activities and it includes voice biofeedback
with multiple sensor fusion technologies. The
ear-lead electrocardiogram measure ECG signals
without any chest belt or sticky tape; it measures
the ECG signals from an ear to an arm. The
characteristics of the smart sensor are to monitor
ear-lead ECG, to play music (possible to change
music styles according to physical motions), to
record GPS coordinates, and to communicate to
a PC by using Bluetooth connection. At the PC,
the data provided by the smart sensor generates
a report on daily activities, and calculates calorie
consumption, heart rate, heart rate variability
(HRV), tracking sport route, time duration, and
average speed. Then, the GPS coordinates infor-
mation is combined with Google Earth for users
to review their activity paths. It can provide real-
time biofeedback which is based on heart rate and
accelerometer signals. The purposes of feedback
are to give voice advice via earphone when current
exercise is overloaded and to select appropriate
music styles automatically by adapting to the
user's physical motion.
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