Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
An adaptive sensor mining framework for pervasive computing applications
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Detection of daily living activities using a two-stage Markov model
Journal of Ambient Intelligence and Smart Environments - Intelligent agents in Ambient Intelligence and smart environments
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Activity recognition in smart environments and healthcare systems is gaining increasing interest. Several approaches are proposed to recognize activities namely intrusive and non-intrusive approaches. This paper presents a new fully non-intrusive approach for recognition of Activities of Daily Living (ADLs) in smart environments. Our approach treats the activity recognition process as an information retrieval problem in which ADLs are represented as hierarchical models, and patterns associated to these ADLs models are generated. A search process for these patterns is applied on the sequences of activities recorded when users perform their daily activities. We show through experiments on real datasets recorded in real smart home how our approach accurately recognizes activities.