F
Fco. Javier Ordóñez
Researcher at Carlos III Health Institute
Publications - 11
Citations - 2351
Fco. Javier Ordóñez is an academic researcher from Carlos III Health Institute. The author has contributed to research in topics: Activity recognition & Hidden Markov model. The author has an hindex of 6, co-authored 11 publications receiving 1772 citations. Previous affiliations of Fco. Javier Ordóñez include University of Sussex & Charles III University of Madrid.
Papers
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Journal ArticleDOI
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
TL;DR: A generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which is suitable for multimodal wearable sensors, does not require expert knowledge in designing features, and explicitly models the temporal dynamics of feature activations is proposed.
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Activity recognition using hybrid generative/discriminative models on home environments using binary sensors.
TL;DR: This paper describes the use of two powerful machine learning schemes, ANN and SVM, within the framework of HMM (Hidden Markov Model), in order to tackle the task of activity recognition in a home setting and shows how the hybrid models achieve significantly better recognition performance.
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Sensor-based Bayesian detection of anomalous living patterns in a home setting
TL;DR: The results suggest that the monitoring system can be used to detect anomalous behavior signs which could reflect changes in health status of the user, thus offering an opportunity to intervene if required.
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Online activity recognition using evolving classifiers
TL;DR: This work describes and evaluates 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.
Journal ArticleDOI
In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models
Fco. Javier Ordóñez,Gwenn Englebienne,Paula de Toledo,Tim van Kasteren,Araceli Sanchis,Ben Kröse +5 more
TL;DR: This article uses Markov Chain Monte Carlo techniques to estimate the parameters of activity recognition models in a Bayesian framework and achieves significantly better recognition performance than a state-of-the-art maximum-likelihood method.