Query customization and trigger optimization on home care systems

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.