Energy-based decision engine for household human activity recognition

Abstract

We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes.

Description

Keywords

Activity recognition, machine learning, energy

Citation

Vafeiadis, A. et al. Energy-based decision engine for household human activity recognition, IEEE Int. Conf. Pervasive Computing and Communication Workshops (PerCom Workshops), Athens, March. 2018.

Rights

Research Institute

Cyber Technology Institute (CTI)
Institute of Artificial Intelligence (IAI)
Institute of Engineering Sciences (IES)