Activity recognition with end-user sensor installation in the home

Abstract

In this work, a system for recognizing activities in the home setting that uses a set of small and simple state-change sensors, machine learning algorithms, and electronic experience sampling is introduced. The sensors are designed to be "tape on and forget" devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Since temporal information is an important component of activities, a new algorithm for recognizing activities that extends the naive Bayes classifier to incorporate low-order temporal relationships was created. Unlike prior work, the system was deployed in multiple residential environments with non-researcher occupants. Preliminary results show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used. Although these preliminary results were based on small datasets collected over a two-week period of time, techniques have been developed that could be applied in future studies and at special facilities to study human behavior such as the MIT Placelab. The system can be easily retrofitted in existing home environments with no major modifications or damage and can be used to enable IT and health researchers to study behavior in the home. Activity recognition is increasingly applied not only in home-based proactive and preventive healthcare applications, but also in learning environments, security systems, and a variety of human-computer interfaces.