Activity recognition in the home setting using simple and ubiquitous sensors

Abstract

During the past several years, researchers have demonstrated that when new wireless sensors are placed in the home environment, data collected from them can be used by software to automatically infer context, such as the activities of daily living. This context-inference can then be exploited in novel applications for healthcare, communication, education, and entertainment. Prior work on automatic context-inference has cleared the way to a reduction in costs associated with manufacturing the sensor technologies and computing resources required by these systems. However, this prior work does not specifically address another major expense of wide-scale deployment of the proposed systems: the expense of expert installation of the sensor devices. To date, most of the context-detection algorithms proposed assume that an expert carefully installs the home sensors and that an expert is involved in acquiring the necessary training examples. End-user sensor installation may offer several advantages over professional sensor installations: 1.) It may greatly reduces the high cost of time required for an expert installation, especially if large numbers of sensors are required for an application, 2.) The process of installing the sensors may give the users a greater sense of control over the technology in their homes, and 3.) End-User Installations also may improve algorithmic performance by leveraging the end-user's domain expertise. An end-user installation method is proposed using "stick on" wireless object usage sensors. The method is then evaluated employing two in-situ, exploratory user studies, where volunteers live in a home fitted with an audio-visual monitoring system. Each participant was given a phone-based tool to help him or her self-install the object usage sensors. They each lived with the sensors for over a week. They were also asked to provide some training data on their everyday activities using multiple methods. Data collected from the two studies is used to qualitatively compare the end-user installation with two professional installation methods. Based on the two exploratory experiments, design guidelines for user self-installation of home sensors are proposed.