DocumentCode
44746
Title
Non-Intrusive Healthcare System in Global Machine-to-Machine Networks
Author
Sang-Joong Jung ; Wan-Young Chung
Author_Institution
Electron. Inf. Commun. Res. Center, Pukyong Nat. Univ., Busan, South Korea
Volume
13
Issue
12
fYear
2013
fDate
Dec. 2013
Firstpage
4824
Lastpage
4830
Abstract
A global machine-to-machine (M2M) healthcare system is proposed to monitor patient´s health conditions using wearable physiological sensors. This system has the potential of providing excellent accessibility to international as well as intercity healthcare services using the concept of IPv6 over low-power wireless personal area networks (6LoWPANs) in a hierarchical network structure. Non-intrusive low-power embedded wearable sensors are designed to dynamically measure health parameters and are connected to the M2M node for wireless transmission through the internet or external IP-enabled networks via the M2M gateway. Practical tests are conducted using the M2M gateway with the IEEE 802.15.4 and the 6LoWPAN protocol in the internal and external networks environments. In addition, the physical health state is estimated by the heart rate variability analysis in the time and frequency domains to gauge the activity of the autonomic nervous system and consequently provide a stress level to the server. Our approaches for the global M2M healthcare system are managed to process the large amount of physiological signals with the network evaluation and to obtain the stress index and autonomic balance diagram of patient´s health conditions.
Keywords
biosensors; health care; neurophysiology; personal area networks; IEEE 802.15.4; IPv6 over low-power wireless personal area networks; M2M node; autonomic nervous system; external IP-enabled networks; global machine-to-machine networks; heart rate variability; hierarchical network structure; internet; nonintrusive healthcare system; patient health conditions; wearable physiological sensors; wireless transmission; Biomedical monitoring; Heart rate variability; Logic gates; Medical services; Sensors; Servers; Stress; IPv6 over low-power wireless personal area networks; Machine-to-machine; autonomic balance diagram; autonomic nervous system; health condition; stress index; wearable physiological sensor;
fLanguage
English
Journal_Title
Sensors Journal, IEEE
Publisher
ieee
ISSN
1530-437X
Type
jour
DOI
10.1109/JSEN.2013.2275186
Filename
6626363
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