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Related Experiment Video

Updated: May 18, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Meeting people's needs in a fully interoperable domotic environment.

Vittorio Miori¹, Dario Russo, Cesare Concordia

¹Institute of Information Science and Technologies A. Faedo (ISTI), CNR-National Research Council of Italy, Pisa, Italy. vittorio.miori@isti.cnr.it

Sensors (Basel, Switzerland)

|September 13, 2012

Summary

Ambient Intelligence (AmI) creates invisible computing environments that learn user habits to improve quality of life. This system enhances domotic interoperability and anticipates health needs through context-aware middleware.

Keywords:

Ambient Intelligent DomoNet XML association rules data mining domotics home environment interoperability machine learning web services

Related Experiment Videos

Last Updated: May 18, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

Computer Science
Human-Computer Interaction
Ubiquitous Computing

Background:

Ambient Intelligence (AmI) relies on context awareness, identifying users and locations for seamless integration.
Computing should be unobtrusive, embedded in the environment, and visible only when necessary.
Existing domotic systems often lack interoperability due to heterogeneous technologies.

Purpose of the Study:

To develop an AmI-based environment that enhances user quality of life.
To create an adaptive, context-aware framework for domotic system interoperability.
To enable AmI systems to learn user habits and anticipate needs.

Main Methods:

Designing a middleware architecture using open standards.
Abstracting underlying heterogeneous technologies for interoperability.
Integrating AmI frameworks with domotic sensors and actuators.

Main Results:

Achieved interoperability between incompatible domotic systems.
Enabled context-aware recognition of user habits and needs.
Facilitated anticipation of unusual situations and health issues.

Conclusions:

The developed AmI framework improves quality of life through intelligent, context-aware environments.
Middleware architecture successfully integrates heterogeneous domotic systems.
AmI systems can proactively address user needs and safety in technological living spaces.