Elements of information theory
Elements of information theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Artificial Intelligence in Medicine
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Assessing recovery from stroke has been so far a time consuming procedure in which highly trained clinicians are required. This paper proposes a mechatronic platform which measures low forces and torques exerted by subjects, Class posterior probabilities are used as a quantitative and statistically sound tool to assess motor recovery from these force and torque measurements. The performance of the patients is expressed in terms of the posterior probability to belong to the class of normal subjects. The mechatronic platform together with the class posterior probabilities enables to automate motor recovery assessment without the need for highly trained clinicians. It is shown that the class posterior probability profiles are highly correlated, r ≈ 0.8, with the well-established Fugl-Meyer scale assessment in motor recovery. These results have been obtained through careful feature subset selection procedures in order to prune the large feature set being generated. The overall approach is general and can be applied to many other health monitoring systems where different categories (diseased vs. healthy) can be identified.