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Figure 14.1. The sensor data mining process.
side medical institutions, even on pervasive devices (e.g., iPhone
heart rate monitor applications that make use of smart phone cam-
eras [5]).
Wearable activity sensors: These sensors measure attributes of
gross user activity, different from narrowly focused vital sign sen-
sors. Good examples are accelerometers used for gait monitoring.
Shoe manufacturers like Nike have enabled many of their running
shoes with sensors capable of tracking walking or jogging activities
[6]. Most smart phones are also equipped with accelerometers and
several wellness management applications leverage these sensors.
Humansensors:Humansplayanintegralroleinthesensingpro-
cess. For instance, physicians introduce important events that
relate to the patient health status during examinations. Lab tech-
nicians follow rigorous processes to provide blood content infor-
mation. Self-reporting (i.e. patients monitoring their health pa-
rameters) is also used in the management of chronic illnesses like
diabetes. More recently, with the emergence of social media and
pervasive computing, people use mechanisms like web-searches and
Twitter to generate reports on important health related events.
Contextual sensors: These sensors are embedded in the environ-
ment around the user to measure different contextual properties.
 
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