Accurate activity recognition in a home setting

TLDR

A sensor system that automatically recognizes activities enables many ubiquitous applications. The paper presents an easy‑to‑install sensor network and a low‑cost annotation method. The study uses an easy‑to‑install sensor network, a 28‑day annotated dataset, and experiments with hidden Markov models and conditional random fields to recognize activities. The system achieves 95.6 % timeslice accuracy and 79.4 % class accuracy.

Abstract

A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.