CUED Publications database

Human Activity Recognition by Combining a Small Number of Classifiers.

Nazabal, A and Garcia-Moreno, P and Artes-Rodriguez, A and Ghahramani, Z (2016) Human Activity Recognition by Combining a Small Number of Classifiers. IEEE J Biomed Health Inform, 20. pp. 1342-1351.

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We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Item Type: Article
Uncontrolled Keywords: Bayes Theorem Human Activities Humans Monitoring, Ambulatory Pattern Recognition, Automated Signal Processing, Computer-Assisted
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:39
Last Modified: 23 Jun 2018 20:13