The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 02, 2016

Filed:

Dec. 17, 2012
Applicant:

Georgia Tech Research Corporation, Atlanta, GA (US);

Inventors:

Shwetak N. Patel, Seattle, WA (US);

Thomas M. Robertson, Atlanta, GA (US);

Gregory D. Abowd, Atlanta, GA (US);

Matthew S. Reynolds, Durham, NC (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01R 19/00 (2006.01); G01R 21/00 (2006.01); H04B 3/54 (2006.01); G06F 17/00 (2006.01);
U.S. Cl.
CPC ...
G01R 21/006 (2013.01); G06F 17/00 (2013.01); H04B 3/544 (2013.01); G05B 2219/40408 (2013.01); H04B 2203/5425 (2013.01); H04B 2203/5458 (2013.01); Y04S 20/38 (2013.01);
Abstract

Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.


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