Paper Info
Title
Evolving activity recognition from sensor streams
Abstract
Recognizing people's activity automatically is an important task that needs to be tackled in order to face other more complex tasks such as action prediction, remote health monitoring, or interventions. Recent research on activity recognition has demonstrated that many different activities can be recognized. In most of these researches, the activities are previously predefined as statistic models over time. However, how people perform a specific activity is changing continuously. In this paper we present an approach for classifying different activities from sensor readings based on Evolving Fuzzy Systems (EFS). Thus, the model that describes an activity evolves according to the changes observed in how that activity is performed. This approach has been successfully tested on a real world domain using binary sensors data streams.
Year
DOI
Venue
2012
10.1109/EAIS.2012.6232812
Evolving and Adaptive Intelligent Systems
Keywords
Field
DocType
fuzzy reasoning,fuzzy systems,pattern recognition,remote sensing,sensors,binary sensor data stream,evolving activity recognition,evolving fuzzy system,people activity recognition,real world domain,remote health monitoring,sensor reading,sensor stream,statistic model,Activity Recognition,Evolving Fuzzy Systems,Sensor Networks
Data mining,Data stream mining,Binary sensors,Activity recognition,Fuzzy reasoning,Statistic,Artificial intelligence,Fuzzy control system,Engineering,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-1726-9
2
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
José Antonio Iglesias126120.54
Francisco Javier Ordóñez22469.05
Agapito Ledezma332631.24
Paula de Toledo417112.84
Araceli Sanchis535740.26
de Toledo, P.650.78