Mobirise


Objetives


General objetive

SAINWEP's main objective is the creation of an open architecture that accelerates the development of software oriented to the healthcare sector and health care and the development of new Big Data technologies based on the analysis of multiple sources of clinical and non-clinical heterogeneous data. 

This architecture aims to cover from the capture of information by IoT devices, storage and analysis with Big Data technology to the exploitation of this information by applications that third parties can design based on the proposed architecture.

Also, the architecture pretends that the applications that use it will be oriented to a specific sector of the population, the elderly and/or with problems related to chronic diseases.  

The proposed project aims to produce business solutions that meet the needs of a wide spectrum of potential customers in the market of wearable technologies, including solutions that benefit from the use of these technologies and solutions that contribute to promote the development of this growing industry. 


Specific Objetives

The project will include advances and developments in various technological fields that will be integrated into a marketable product and provided through a digital platform. It is articulated on the following specific technical objectives that are detailed below:
  • Detect and identify the usual components of the care and geriatric applications that exists in the market in order to study their adaptation to the HL7 or ICD-10 standards.
  • Detect and identify the different FIWARE enablers that could be included as project components.
  • Create the learning components that will be fed from the analysis of large volumes of data.
  • Create a software components library under the FIWARE platform, such as data access, user interface, data exchange, interoperability, automatic reading, visual protocols, etc.
  • Develop a data taxonomy that facilitates the input to the analysis module of data from the various components of the library.
  • Detect and specify the information coming from the analysis module in order to identify the information that can be used by the architecture components. Classification of information.
  • Development of a back end interface system, which allows the integrated components in the platform to be fed from the analysis module and vice versa.
  • Development of new Big Data technologies oriented to the storage of multiple heterogeneous data.
  • Development of new algorithms to be applied to health Big Data Analytics, which imply the use of new non-clinical variables, such as environmental temperature, daily activity data, etc.
Flujo de trabajo