Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Modeling and control of non-linear systems using soft computing techniques
Applied Soft Computing
Computers in Biology and Medicine
Robust, low-cost, non-intrusive sensing and recognition of seated postures
Proceedings of the 20th annual ACM symposium on User interface software and technology
Computers in Biology and Medicine
A sensing seat for human authentication
IEEE Transactions on Information Forensics and Security
Computers in Biology and Medicine
Neural network analysis of employment history as a risk factor for prostate cancer
Computers in Biology and Medicine
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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Objective: In this paper we address the problem of recognising the movement intentions of patients restricted to a medical bed. The developed recognition system will be used to implement a natural human-machine interface to move a medical bed by means of the slight movements of patients with reduced mobility. Methods and material: Our proposal uses pressure map sequences as input and presents a novel system based on artificial neural networks to recognise the movement intentions. The system analyses each pressure map in real-time and classifies the raw information into output classes which represent these intentions. The complexity of the recognition problem is high because of the multiple body characteristics and distinct ways of communicating intentions. To address this problem, a complete processing chain was developed consisting of image processing algorithms, a knowledge extraction process, and a multilayer perceptron (MLP) classification model. Results: Different configurations of the MLP have been investigated and quantitatively compared. The accuracy of our approach is high, obtaining an accuracy of 87%. The model was compared with five well-known classification paradigms. The performance of a reduced model, obtained by through feature selection algorithms, was found to be better and less time-consuming than the original model. The whole proposal has been validated with real patients in pre-clinical tests using the final medical bed prototype. Conclusions: The proposed approach produced very promising results, outperforming existing classification approaches. The excellent behaviour of the recognition system will enable its use in controlling the movements of the bed, in several degrees of freedom, by the patient with his/her own body.