TY - GEN
T1 - Query customization & trigger optimization on home care systems
AU - Zhuang, Yung Yu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - Epilepsy is a common neural disorder disease, while difficult to cure. There is still a risk of suffering from seizures even though patients have used antiepileptic drugs or had an operation. In such cases, patients must be immediately taken care; on the other hand, how to avoid introducing danger to their family and people around is also a concern. To address this issue, we integrate and develop a health cloud system to detect, record, and further predict epileptic seizure, which includes a dedicated wearable device for detection, a medical-IoT box to avoid heavy computation on the wearable device and provide cross reference to cameras, a cloud computing platform for complex computation, and an application on tablets for health care professionals. We propose a reactive and highly programmable model for such a system to allow health care professionals to easily and quickly query data from different devices and customize the trigger conditions, while optimize computing resource to achieve power saving on devices. We base this research on reactive programming (RP), which recently attracts the interests of researchers and developers, to construct our model, and develop a domain-specific language (DSL) that is applied among the medical-IoT box, cloud computing, and user interface for health care professionals. Such a DSL must be easy-To-write since health care professionals are not necessarily experts in programming, but it must also be powerful enough to allow them to query the logging data, analyze the interaction between different devices, and further configure the setting of devices for individual patients to benefit from our platform. At the same time, it automatically optimizes the communication and computation based the trigger conditions to achieve power saving on devices.
AB - Epilepsy is a common neural disorder disease, while difficult to cure. There is still a risk of suffering from seizures even though patients have used antiepileptic drugs or had an operation. In such cases, patients must be immediately taken care; on the other hand, how to avoid introducing danger to their family and people around is also a concern. To address this issue, we integrate and develop a health cloud system to detect, record, and further predict epileptic seizure, which includes a dedicated wearable device for detection, a medical-IoT box to avoid heavy computation on the wearable device and provide cross reference to cameras, a cloud computing platform for complex computation, and an application on tablets for health care professionals. We propose a reactive and highly programmable model for such a system to allow health care professionals to easily and quickly query data from different devices and customize the trigger conditions, while optimize computing resource to achieve power saving on devices. We base this research on reactive programming (RP), which recently attracts the interests of researchers and developers, to construct our model, and develop a domain-specific language (DSL) that is applied among the medical-IoT box, cloud computing, and user interface for health care professionals. Such a DSL must be easy-To-write since health care professionals are not necessarily experts in programming, but it must also be powerful enough to allow them to query the logging data, analyze the interaction between different devices, and further configure the setting of devices for individual patients to benefit from our platform. At the same time, it automatically optimizes the communication and computation based the trigger conditions to achieve power saving on devices.
KW - Domain-specific language
KW - Epileptic seizure detection
KW - Health cloud platform
KW - Reactive programming
UR - http://www.scopus.com/inward/record.url?scp=85028555666&partnerID=8YFLogxK
U2 - 10.1109/ICASI.2017.7988514
DO - 10.1109/ICASI.2017.7988514
M3 - 會議論文篇章
AN - SCOPUS:85028555666
T3 - Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017
SP - 668
EP - 671
BT - Proceedings of the 2017 IEEE International Conference on Applied System Innovation
A2 - Meen, Teen-Hang
A2 - Lam, Artde Donald Kin-Tak
A2 - Prior, Stephen D.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Applied System Innovation, ICASI 2017
Y2 - 13 May 2017 through 17 May 2017
ER -