Authors:
Maiya Hori
;
Tatsuro Harada
and
Rin-ichiro Taniguchi
Affiliation:
Kyushu University, Japan
Keyword(s):
Safety, Anomaly Detection, People Activity Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Economics, Business and Forecasting Applications
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
Abstract:
We propose an anomaly detection method for watching elderly people using only the power data acquired by a
smart meter. In a conventional system that uses only power data, a warning is issued if the power consumption
does not increase after the wake-up time or when the amount of power does not change for a long time. These
methods need to set the wake-up time and power threshold for each user. Furthermore, wrong warnings are
issued while residents are out of the home. In our method, multiple common power consumption models are
created for each household for each short time zone, and a watching system is constructed by regarding the
gaps between these models and newly observed data as anomaly values. This can be automatically applied to
various situations such as “during sleep,” “during home activity” and “time zone for frequently going out in
the daytime.”