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Evolving Human Activity Classifier from Sensor Streams

Research output: Contribution in Book/Report/ProceedingsPaper

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Publication date04/2011
Host publicationEvolving and Adaptive Intelligent Systems (EAIS), 2011 IEEE Workshop on
PublisherIEEE
Pages139-146
Number of pages8
ISBN (Print)978-1-4244-9978-6
<mark>Original language</mark>English

Conference

ConferenceIEEE
CountryFrance
CityParis
Period11/04/1115/04/11

Conference

ConferenceIEEE
CountryFrance
CityParis
Period11/04/1115/04/11

Abstract

Human activity recognition in intelligent environments is a very important task for many applications such as assisted living or surveillance. In order to make those
environments sensitive to people, it is necessary to recognize and track the activities that they perform as part of their daily routines. Most of the current approaches for recognizing human activities do not consider the changes in how a human performs a speci®c activity. Those approaches rely on prede®ned activities which are represented as static models over time.
In this paper, we propose an automated approach to track and recognize daily activities from sensor streams. Any activity is represented in this research as a sequence of raw sensors data. These sequences are treated using statistical methods in order to discover activity patterns. However, these patterns change
due to the dynamic nature of human activities. Therefore, as the way to perform an activity is usually not ®xed but it changes and evolves, we propose a human activity recognition method based on Evolving Systems.