| Ubiquitous Sensors (2003) | |||||||||||||||
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, | |||||||||||||||
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