EnergyLens: Combining Smartphones with Electricity Meter for Accurate Activity Detection and User Annotation Manaswi Sahay, Shailja Thakury, Amarjeet Singhy, Yuvraj Agarwalz yIndraprastha Institute of Information Technology, Delhi zCarnegie Mellon University y{manaswis, shailja1275, amarjeet}@iiitd.ac.in,
[email protected] ABSTRACT Categories and Subject Descriptors Inferring human activity is of interest for various ubiquitous C.3 [Special-Purpose and Application-Based Systems]: computing applications, particularly if it can be done using Real-time and embedded systems ambient information that can be collected non intrusively. In this paper, we explore human activity inference, in the context of energy consumption within a home, where we de- Keywords fine an \activity" as the usage of an electrical appliance, its Smart Meters; Smartphones; Energy Disaggregation; Activ- usage duration and its location. We also explore the dimen- ity Detection; User Association sion of identifying the occupant who performed the activity. Our goal is to answer questions such as \Who is watching TV in the Dining Room and during what times?". This 1. INTRODUCTION Smartphones, over the past few years, have seen unprece- information is particularly important for scenarios such as dented growth across the world. In many markets, such as the apportionment of energy use to individuals in shared the US, they have long since surpassed sales of traditional settings for better understanding of occupant's energy con- feature phones. While many factors have contributed to sumption behavioral patterns. Unfortunately, accurate ac- their popularity, the most important perhaps is the increase tivity inference in realistic settings is challenging, especially in device capabilities as supported by higher end components when considering ease of deployment.