IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR.

Area of Science:

• Pervasive healthcare
• Internet of Medical Things (IoMT)
• Health informatics standards

Background:

• Pervasive healthcare services are evolving with advancements in communication technologies like M2M and IoT.
• OpenEHR standardizes electronic health records but lacks interoperability for M2M/IoT e-health devices.
• Data heterogeneity in e-health hinders advanced data processing techniques like data mining and OLAP.

Purpose of the Study:

• To propose an Internet of Medical Things (IoMT) platform for pervasive healthcare.
• To ensure interoperability, data quality, and scalability in M2M-based architectures.
• To facilitate high-volume data processing, knowledge extraction, and common healthcare services.

Main Methods:

• Developed an IoMT platform leveraging M2M architecture.
• Utilized OpenEHR semantics for data quality evaluation and standardization.
• Integrated Hadoop Map/Reduce for big data techniques and OLAP.
• Employed Fast Healthcare Interoperability Resource (FHIR) APIs for content sharing.

Main Results:

• The proposed IoMT platform ensures interoperability and scalability for pervasive healthcare.
• It enables standardized healthcare data storage through the association of IoMT devices and OpenEHR observations.
• Facilitates the application of big data techniques and online analytic processing (OLAP).

Conclusions:

• The IoMT platform effectively addresses interoperability challenges in pervasive healthcare.
• It enhances data quality and enables advanced analytics for e-health data.
• The platform supports seamless data sharing and processing in modern healthcare ecosystems.