.png){kind=icon}
Return
Online activity recognition using evolving classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2012.08.066.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper describes the use of evolving classifiers for activity recognition from sensor readings in ambient assisted living environments. Recognizing the activities an elderly person who lives alone performs, and identifying potential problems from the detected activities is a very active topic of research. However, current approaches do not take into account the fact that the way an activity is performed by a person evolves over time and therefore activities are identified by mapping them to a static model. In this work we describe and evaluate an approach for online classifying based on Evolving Fuzzy Systems (EFS): activities are described by a model that evolves over time, according to the changes observed in the way an activity is performed. These classifiers have been evaluated on three datasets obtained from real home settings, achieving a good recognition performance, at a confidence interval of 95%, compared with well know probabilistic models in terms of F-Measure, but improving their performance in terms of online capabilities and ability to adapt to the evolving ways in which activities are carried out. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Activity recognition
Evolving Fuzzy Systems
Sensor networks
Journal
IF:
7.5
Papers:
3.0W
Citations:
12.6W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveLack of Efficacy of Drug Therapy in Preventing Takotsubo Cardiomyopathy Recurrence: A Meta‐analysis
OAAI
OAAIShareSaveShareSave
View more