Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
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
Robust recognition of physical team behaviors using spatio-temporal models
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Bayesian-based scenario generation method for human activities
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
Latent-Dynamic Conditional Random Fields for recognizing activities in smart homes
Journal of Ambient Intelligence and Smart Environments - Ambient and Smart Component Technologies for Human Centric Computing
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One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this application. It is essentially a temporal classification problem which has been modeled in the past by naïve Bayes classifiers and hidden Markov models (HMMs). In this paper, we describe the use of another probabilistic model: Conditional Random Fields (CRFs), which is currently gaining popularity for its remarkable performance for activity recognition. Our focus is on using CRFs to recognize activities performed by an inhabitant in a smart home environment and our goal is to validate the claim of its higher or similar performance by comparing CRFs with HMMs using data collected in a real smart home.