@conference{3479e0cb51ac487988fa777741cf8836,
title = "A Mobile Cloud-Based Biofeedback Platform for Evaluating Medication Response: 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017",
abstract = "In recent years, biofeedback has been widely applied into diagnosis and treatment of various diseases. There are also increasingly research exploiting various ICT (Information & Communication Technology) technologies, such as cloud technology, to achieve diagnosis and treatment. Therefore, how to use mobile cloud technology to assist the disease's diagnosis, to record treatment status, and to infer the result will be an important issue. In this paper, we will propose a mobile cloud platform and framework for the patient of mental illness for evaluating medication response through a variety of biofeedback information collection, integration, and fusion, so that physician can know the patient's situation. The physiological data including Heart Rate Variability and Brain Wave are collected through wearable sensors. And the psychological data is collected through monthly mood chart. The biofeedback physiological and psychological data can be fused into together to show the medication response after patient taking some medicines. An APP for the framework has been developed to show the effectiveness. {\textcopyright} 2017 IEEE.",
keywords = "Biofeedback, Cloud computing, Mobile Cloud, Diagnosis, Physiological models, Physiology, Brain wave, Cloud technologies, Communication technologies, Heart rate variability, Information collections, Mental illness, Mobile clouds, Physiological data, Diseases",
author = "Y.-Z. Lai and C.-H. Tai and Y.-S. Chang and K.-H. Chung",
note = "Conference code: 135287 Export Date: 20 October 2018 Funding details: 105-2634-F-305 -001 Funding details: 105-2221-E-305 -010 Funding details: 106-2221-E-305-014 Funding details: MOST, Endocrine Society of the Republic of China Funding details: MOST, Ministry of Science and Technology, Taiwan Funding text: ACKNOWLEDGMENT This work was partially supported by Ministry of Science and Technology of Taiwan, Republic of China under Grant No. MOST 105-2634-F-305 -001, 105-2221-E-305 -010, and 106-2221-E-305-014. 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year = "2018",
doi = "10.1109/SC2.2017.35",
language = "English",
pages = "183--188",
}
