HUMAN ACTIVITY RECOGNITION BASED ON EVOLVING FUZZY SYSTEMS

被引:51
|
作者
Antonio Iglesias, Jose [1 ]
Angelov, Plamen [2 ]
Ledezma, Agapito [1 ]
Sanchis, Araceli [1 ]
机构
[1] Univ Carlos III Madrid, Madrid 28914, Spain
[2] Univ Lancaster, InfoLab21, Lancaster LA1 4WA, England
基金
英国工程与自然科学研究理事会;
关键词
Activity recognition; evolving fuzzy systems; Fuzzy-Rule-Based (FRB) classifiers; DELAYED NEURAL-NETWORKS; CLASSIFICATION; MODEL; SYNCHRONIZATION; IMPROVEMENT; STABILITY;
D O I
10.1142/S0129065710002462
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.
引用
收藏
页码:355 / 364
页数:10
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