Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Wireless Sensor Networks for Health Monitoring
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
The class imbalance problem: A systematic study
Intelligent Data Analysis
Online classification of nonstationary data streams
Intelligent Data Analysis
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Efficient duration and hierarchical modeling for human activity recognition
Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Monitoring and modeling simple everyday activities of the elderly at home
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
An activity monitoring system for elderly care using generative and discriminative models
Personal and Ubiquitous Computing
Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transferring knowledge of activity recognition across sensor networks
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Evolving intelligent algorithms for the modelling of brain and eye signals
Applied Soft Computing
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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.