Stress Detection Using Frequency Spectrum Analysis of Wrist-Measured Electrodermal Activity.

The article deals with the detection of stress using the electrodermal activity (EDA) signal measured at the wrist. We present an approach for feature extraction from EDA. The approach uses frequency spectrum analysis in multiple frequency bands.

Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning.

Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients' and healthcare system's proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors.

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems.

This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user's intention during the dialog.

Predicting Protein Relationships to Human Pathways through a Relational Learning Approach Based on Simple Sequence Features.

Unlabelled: Biological pathways are important elements of systems biology and in the past decade, an increasing number of pathway databases have been set up to document the growing understanding of complex cellular processes. Although more genome-sequence data are becoming available, a large fraction of it remains functionally uncharacterized. Thus, it is important to be able to predict the mapping of poorly annotated proteins to original pathway models.

Activity recognition using hybrid generative/discriminative models on home environments using binary sensors.

Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach.

Simulating visual qualia in the CERA-CRANIUM cognitive architecture.

The concept of qualia poses a central problem in the framework of consciousness studies. Despite it being a controversial issue even in the study of human consciousness, we argue that qualia can be complementarily studied using artificial cognitive architectures. In this work we address the problem of defining qualia in the domain of artificial systems, providing a model of "artificial qualia".

Human activity recognition based on Evolving Fuzzy Systems.

Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity.

Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques.

Background: Outcome prediction for subarachnoid hemorrhage (SAH) helps guide care and compare global management strategies. Logistic regression models for outcome prediction may be cumbersome to apply in clinical practice.

Objective: To use machine learning techniques to build a model of outcome prediction that makes the knowledge discovered from the data explicit and communicable to domain experts.