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AsthmaGuide: An asthma monitoring and advice ecosystem

Title
AsthmaGuide: An asthma monitoring and advice ecosystem
Authors
Ra, Ho-KyeongSalekin, AsifYoon, Hee JungKim, JeremyNirjon, ShahriarStone, David J.Kim, SujeongLee, Jong-MyungSon, Sang HyukStankovic, John A.
DGIST Authors
Ra, Ho-Kyeong; Yoon, Hee Jung; Son, Sang Hyuk
Issue Date
2016-10-26
Citation
2016 IEEE Wireless Health, WH 2016, 128-135
Type
Conference
Article Type
Conference Paper
ISBN
9781509030903
Abstract
Recently, there has been an increased use of wireless sensor networks and embedded systems in the medical sector. Healthcare providers are now attempting to use these devices to monitor patients in a more accurate and automated way. This would permit healthcare providers to have up-todate patient information without physical interaction, allowing for more accurate diagnoses and better treatment. One group of patients that can greatly benefit from this kind of daily monitoring is asthma patients. Healthcare providers need daily information in order to understand the current risk factors for asthma patients and to provide appropriate advice. It is not only important to monitor patients' lung health, but also to monitor other physiological parameters, environmental factors, medication, and subjective feelings. We develop a smartphone, sensor rich, and cloud based asthma system called AsthmaGuide, in which a smartphone is used as a hub for collecting comprehensive information. The data, including data over time, is then displayed in a cloud web application for both patients and healthcare providers to view. AsthmaGuide also provides an advice and alarm infrastructure based on the collected data and parameters set by healthcare providers. With these components, AsthmaGuide provides a comprehensive ecosystem that allows patients to be involved in their own health and also allows doctors to provide more effective day-today care. Using real asthma patient wheezing sounds, we also develop two different types of classification approaches and show that one is 96% accurate, the second is 98.6% accurate and both outperform the state of art which is 87% accurate at automatically detecting wheezing. AsthmaGuide has both English and Korean language implementations. © 2016 IEEE.
URI
http://hdl.handle.net/20.500.11750/4518
DOI
10.1109/WH.2016.7764567
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Son, Sang Hyuk RTCPS(Real-Time Cyber-Physical Systems Research) Lab
  • Research Interests
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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