DocumentCode :
741291
Title :
Guest Editorial Sensor Informatics and Quantified Self
Author :
Picard, Rosalind ; Wolf, Gary
Author_Institution :
, MIT Media Lab, Cambridge, MA, USA
Volume :
19
Issue :
5
fYear :
2015
Firstpage :
1531
Lastpage :
1531
Abstract :
The articles in this special issue focus on new technologies and applications for medical services that incorporate wearable sensors, signal processing, machine learning, and data mining techniques. The ability to collect large sets of human data comfortably 24/7, are advancing new ways to learn about human well being. Measurements that used to be confined to short-term sampling in a lab or medical facility are now able to be conducted continuously, while at home, work, sleep, or play. Studies are no longer limited to a focus on disease progression or to the effect of therapeutic measures provided in clinical settings???instead, it is becoming possible to quantify healthy activities and behavior, and capture how these slowly change as illness develops or progresses. The quantified self movement, where people can monitor their own health and fitness-related data, is closely linked to the emergence of new methods in biometric sensing.
Keywords :
Biomedical monitoring; Biomedical signal processing; Data mining; Medical services; Special issues and sections; Text analysis; Wearable sensors;
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/JBHI.2015.2462372
Filename :
7235004
Link To Document :
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