Summary
Objectives:
To many people, home is a sanctuary. With the maturing of smart home technologies,
many people with cognitive and physical disabilities can lead independent lives in
their own homes for extended periods of time. In this paper, we investigate the design
of machine learning algorithms that support this goal. We hypothesize that machine
learning algorithms can be designed to automatically learn models of resident behavior
in a smart home, and that the results can be used to perform automated health monitoring
and to detect anomalies.
Methods:
Specifically, our algorithms draw upon the temporal nature of sensor data collected
in a smart home to build a model of expected activities and to detect unexpected,
and possibly health-critical, events in the home.
Results:
We validate our algorithms using synthetic data and real activity data collected
from volunteers in an automated smart environment.
Conclusions:
The results from our experiments support our hypothesis that a model can be learned
from observed smart home data and used to report anomalies, as they occur, in a smart
home.
Keywords
Machine learning - smart homes - anomaly detection - temporal relations
