TY - GEN
T1 - Autonomic fall detection system
AU - Ozdemir, Ahmet Turan
AU - Tunc, Cihan
AU - Hariri, Salim
N1 - Funding Information:
ACKNOWLEDGMENT This work is partly supported by National Science Foundation (NSF) research project NSF CNS-1624668, Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) award number FA95550-12-1-0241, and Thomson Reuters in the framework of the Partner University Fund (PUF) project (PUF is a program of the French Embassy in the United States and the FACE Foundation and is supported by American donors and the French government), and Erciyes University Scientific Research Project Coordination Department under grant number FBA-11-3579.
Funding Information:
This work is partly supported by National Science Foundation (NSF) research project NSF CNS-1624668
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/9
Y1 - 2017/10/9
N2 - Internet of Things (IoT) and cloud computing have produced major breakthroughs in information services and applications. Internet provided the required connections to countless devices or things that have led to a significant improvement in the quality of life. In addition, using wearable devices and smart sensors has facilitated the deployment of pervasive healthcare services that provided better healthcare, and reduced life threatening risks without affecting one's privacy. In this paper, we present an architectural approach for autonomic healthcare management system and show how to use this system for autonomic fall detection, where we leverage wearable technologies, IoT, and cloud computing.
AB - Internet of Things (IoT) and cloud computing have produced major breakthroughs in information services and applications. Internet provided the required connections to countless devices or things that have led to a significant improvement in the quality of life. In addition, using wearable devices and smart sensors has facilitated the deployment of pervasive healthcare services that provided better healthcare, and reduced life threatening risks without affecting one's privacy. In this paper, we present an architectural approach for autonomic healthcare management system and show how to use this system for autonomic fall detection, where we leverage wearable technologies, IoT, and cloud computing.
KW - cloud computing
KW - data analytics
KW - fall detection
KW - fog computing
KW - internet of things
KW - wearable devices
UR - http://www.scopus.com/inward/record.url?scp=85035212870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035212870&partnerID=8YFLogxK
U2 - 10.1109/FAS-W.2017.142
DO - 10.1109/FAS-W.2017.142
M3 - Conference contribution
AN - SCOPUS:85035212870
T3 - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
SP - 166
EP - 170
BT - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
Y2 - 18 September 2017 through 22 September 2017
ER -