Lorena Álvarez

TL;DR: The preliminary results obtained from the data analysis collected during the first phase of the ATREC project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax.

TL;DR: Experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.

TL;DR: This paper presents some of the first results in the development of an automatic sound classification algorithm for hearing aids, and a divide and conquer strategy is proposed, resulting thus in a layered structure.

TL;DR: The proposed feature-selection algorithm selects a feature subset composed of only 21 features, much smaller than the 76 features of the complete, original set of available features, saving a great number of the scarce computational resources, and making possible to put into practice the concept at reasonable cost.

TL;DR: In this paper, the authors focus on the development of an automatic sound classifier for digital hearing aids that aims to enhance the listening comprehension when the user goes from a sound environment to another different one.