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. We present 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. We use machine learning techniques to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. We tested our system in one home for several weeks and in five homes for one week each to evaluate the system performance over time and in different types of houses. Results indicate that we can learn and classify various electrical events with accuracies ranging from 85-90%.
['Shwetak N. Patel', 'Thomas Robertson', 'Julie A. Kientz', 'Matthew S. Reynolds', 'Gregory D. Abowd']
At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award)