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

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Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks.

Prafulla N Dawadi¹, Diane J Cook¹, Maureen Schmitter-Edgecombe²

¹School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA.

IEEE Transactions on Systems, Man, and Cybernetics. Systems

|December 23, 2014

Summary

This summary is machine-generated.

Smart home technology can automatically assess resident well-being by tracking daily activities. This machine learning method accurately estimates functional health and predicts cognitive health, aiding in understanding everyday well-being.

Keywords:

Machine learning Smart environments

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Area of Science:

Artificial Intelligence
Gerontology
Human-Computer Interaction

Background:

Intelligent systems offer automated resident well-being assessment.
Functional health, the ability to perform activities independently, is crucial for well-being.
Smart home technologies provide a platform for unobtrusive activity monitoring.

Purpose of the Study:

To introduce a machine learning-based method for assessing activity quality in smart homes.
To validate the automated assessment of activity quality against direct observation.
To predict cognitive health based on smart home activity quality.

Main Methods:

Developed a machine learning model to assess activity quality using smart home sensor data.
Recruited 179 participants to perform complex activities in a smart home apartment.
Quantified activity quality and correlated it with direct observation scores.

Main Results:

Observed a statistically significant correlation (r=0.79) between automated and direct observation scores for activity quality.
Achieved an Area Under the Curve (AUC) of 0.64 for predicting cognitive health using machine learning based on task quality.
Demonstrated the feasibility of using smart home data for functional health assessment.

Conclusions:

Automated assessment of activity quality in smart homes is a valid approach.
This technology can contribute to understanding everyday functional health.
Machine learning analysis of smart home activities is a promising tool for cognitive health prediction.