Conference Proceeding

Evolving human activity classifier from sensor streams

Carlos III Univ. of Madrid, Leganes, Spain
05/2011; DOI:10.1109/EAIS.2011.5945921 pp.139 - 146 In proceeding of: Evolving and Adaptive Intelligent Systems (EAIS), 2011 IEEE Workshop on
Source: IEEE Xplore

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 specific activity. Those approaches rely on predefined 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 fixed but it changes and evolves, we propose a human activity recognition method based on Evolving Systems.

0 0
 · 
0 Bookmarks
 · 
40 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Full-text

View
0 Downloads
Available from

Keywords

activity patterns
 
approaches
 
automated approach
 
current approaches
 
dynamic nature
 
Evolving Systems
 
human activities
 
Human activity recognition
 
human activity recognition method
 
intelligent environments
 
patterns change
 
predefined activities
 
raw sensors data
 
routines
 
sensor streams
 
sequences
 
statistical methods
 
surveillance