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Quantitative Indicators for Behaviour Drift Detection from Home Automation Data.

Fabio Veronese¹, Andrea Masciadri¹, Sara Comai¹

¹Department of Electronics, Information and Bioengineering, Politecnico di Milano - via Anzani 42, 22100, Como, Italy.

Studies in Health Technology and Informatics

|September 7, 2017

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Smart Homes enable elderly monitoring by analyzing inhabitant behavior through home automation data. This approach quantifies behavior changes, promoting senior independence and reducing care costs.

Area of Science:

Gerontology
Ubiquitous Computing
Behavioral Science

Background:

The increasing elderly population necessitates innovative solutions for independent living.
Smart home technology offers a platform for unobtrusive monitoring of seniors.
Current monitoring methods may be costly or intrusive, impacting quality of life.

Purpose of the Study:

To explore the potential of smart home data for elderly monitoring.
To develop quantitative indicators for extracting inhabitant behavior from home automation data.
To demonstrate the effectiveness of this approach in identifying behavioral changes.

Main Methods:

Utilizing data from home automation systems within smart home environments.
Developing and applying quantitative indicators to analyze inhabitant behavior patterns.

Keywords:

Behaviour Detection Smart Home home care and monitoring

Related Experiment Videos

Correlating extracted behavioral data with known changes or events.

Main Results:

Home automation data contains valuable information about inhabitant behavior.
Quantitative indicators can effectively extract and analyze behavioral patterns.
The proposed approach successfully evidenced significant behavior changes in inhabitants.

Conclusions:

Smart home technology is a viable tool for elderly monitoring.
Behavioral analysis of home automation data can support extended independent living for seniors.
This method offers a cost-effective and non-intrusive approach to elder care.