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Updated: Apr 25, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Active in-database processing to support ambient assisted living systems
Wagner O de Morais1, Jens Lundström2, Nicholas Wickström3
1School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, Halmstad 30118, Sweden. wagner.demorais@hh.se.
This study introduces a novel database-centric architecture for smart home and ambient assisted living (AAL) systems. It enhances security and privacy by processing sensitive data within the database, improving system performance and maintainability.
Area of Science:
- Computer Science
- Health Informatics
- Artificial Intelligence
Background:
- Existing software architectures for smart homes and ambient assisted living (AAL) systems primarily use database management systems (DBMSs) for data storage only.
- This approach presents limitations in terms of data processing efficiency, security, and privacy for sensitive user data.
Purpose of the Study:
- To propose and validate a database-centric architecture for AAL systems that leverages active databases and in-database processing.
- To enhance the performance, security, and privacy of AAL systems by centralizing data processing within the DBMS.
Main Methods:
- Development of a database-centric architecture utilizing active databases with triggers for event detection and in-database processing via stored procedures and functions.
- Implementation and testing of three distinct AAL services to demonstrate the feasibility and flexibility of the proposed architecture.
- Application of in-database machine learning methods for modeling user behaviors, such as bed-exits and room transitions.
Main Results:
- The active in-database processing approach successfully detected events like bed-exits and room transitions, and modeled early night behaviors.
- Centralizing computation within the DBMS eliminated the need to transfer sensitive data externally, significantly improving performance, security, and privacy.
- The architecture demonstrated improved code reuse, adaptation, and maintenance, crucial for evolving smart home environments.
Conclusions:
- Database-centric architectures with active and in-database processing offer a scalable, secure, and privacy-preserving solution for AAL systems.
- DBMSs can effectively address the complex requirements of smart environments in healthcare, including dependability and personalization.
- This approach is well-suited to the heterogeneous nature of users, needs, and devices characteristic of smart homes and AAL systems.
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