FixtureFinder

Abstract

The monitoring of electrical and water fixtures in the home is being applied for a variety of “smart home” applications, such as recognizing activities of daily living (ADLs) or conserving energy or water usage. Fixture monitoring techniques generally fall into two categories: fixture recognition and fixture disaggregation. However, existing techniques require users to explicitly identify each individual fixture, either by placing a sensor on it or by manually creating training data for it. In this paper, we present a new fixture discovery system that automatically infers the existence of electrical and water fixtures in the home. We call the system FixtureFinder. The basic idea is to use data fusion between the smart meters and other sensors or infrastructure already in the home, such as the home security or automation system, and to find repeating patterns in the fused data stream. To evaluate FixtureFinder, we deployed the system into 4 different homes for 7-10 days of data collection. Our results show that FixtureFinder is able to identify and differentiate major light and water fixtures in less than 10 days, including multiple copies of light bulbs and sinks that have identical power/water profiles.