RFID-based techniques for human-activity detection
Communications of the ACM - Special issue: RFID
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Practical Lessons from Place Lab
IEEE Pervasive Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Common sense based joint training of human activity recognizers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Detection Of Normal and Novel Behaviours In Ubiquitous Domestic Environments
The Computer Journal
Towards an integrated approach for task modeling and human behavior recognition
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction design and usability
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
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In this paper we present a feasibility study regarding the recognition of high level daily living and care activities. We examine a hybrid discriminative and model based generative approach based on RFID and inertial sensor data. We show that the presented sensor configuration is able to deliver sensor readings and object sightings at a sufficient rate without forcing user compliance. We further evaluated the advantage of a model based approach over a static classifier, compared the individual contribution of each sensor type and could reach accuracy rates of 97% and 85%.