Robust Multi-Scenario Speech-Based Emotion Recognition System.

Every human being experiences emotions daily, e.g., joy, sadness, fear, anger.

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors.

Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust.

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study.

Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical.

Knowledge and Opinion of Obstetrician-Gynecologists and Midwives Regarding Complementary and Alternative Medicine in Spain.

The objective of this article is to present the results from a cross-sectional survey carried out to assess and compare knowledge, attitudes, and beliefs of the obstetrician-gynecologists and midwives, regarding a set of complementary and alternative therapies in the area of the Corredor del Henares in Spain. The results show a high percentage of acceptance regarding complementary and alternative therapies in the field of obstetrics and gynecology, and more than half of the Spanish professionals of reproductive health would like to learn more about these therapies.

Smart Sound Processing for Defect Sizing in Pipelines Using EMAT Actuator Based Multi-Frequency Lamb Waves.

Pipeline inspection is a topic of particular interest to the companies. Especially important is the defect sizing, which allows them to avoid subsequent costly repairs in their equipment. A solution for this issue is using ultrasonic waves sensed through Electro-Magnetic Acoustic Transducer (EMAT) actuators.

Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy.

Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements.

Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones.

Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals.

A Computationally Efficient Sound Environment Classifier for Hearing Aids.

A computationally efficient system for sound environment classification in digital hearing aids is presented in this paper. The goal is to automatically classify three different listening environments: "speech," "music," and "noise." The system is designed considering the computational limitations found in such devices.

Influence of acupuncture on the third stage of labor: a randomized controlled trial.

Introduction: A prolonged third stage of labor is considered to be a risk factor for postpartum hemorrhage. The objective of this study was to determine the ability of acupuncture to reduce the length of the third stage of labor.

Methods: Seventy-six puerperal women who had a normal spontaneous birth at the Hospital Universitario Principe de Asturias, Alcalá de Henares, Spain, were included in a single-blind randomized trial and evaluated by a third party.

Wearable biomedical measurement systems for assessment of mental stress of combatants in real time.

The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the "Coincidente" program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information.

Sensorized garments and textrode-enabled measurement instrumentation for ambulatory assessment of the autonomic nervous system response in the ATREC project.

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others.

Cole parameter estimation from total right side electrical bioimpedance spectroscopy measurements--influence of the number of frequencies and the upper limit.

Applications based on measurements of Electrical Bioimpedance Spectrocopy (EBIS) analysis are proliferating. The most spread and known application of EBIS is the non-invasive assessment of body composition. Fitting to the Cole function to obtain the Cole parameters, R(0) and R(∞), is the core of the EBIS analysis to obtain the body fluid distribution.

Cole parameter estimation from electrical bioconductance spectroscopy measurements.

Several applications of Electrical Bioimpedance (EBI) make use of Cole parameters as base of their analysis, therefore Cole parameters estimation has become a very common practice within Multifrequency- and EBI spectroscopy. EBI measurements are very often contaminated with the influence of parasitic capacitances, which contributes to cause a hook-alike measurement artifact at high frequencies in the EBI obtained data. Such measurement artifacts might cause wrong estimations of the Cole parameters, contaminating the whole analysis process and leading to wrong conclusions.

Cole equation and parameter estimation from electrical bioimpedance spectroscopy measurements - A comparative study.

Since there are several applications of Electrical Bioimpedance (EBI) that use the Cole parameters as base of the analysis, to fit EBI measured data onto the Cole equation is a very common practice within Multifrequency-EBI and spectroscopy. The aim of this paper is to compare different fitting methods for EBI data in order to evaluate their suitability to fit the Cole equation and estimate the Cole parameters. Three of the studied fittings are based on the use of Non-Linear Least Squares on the Cole model, one using the real part only, a second using the imaginary part and the third using the complex impedance.

Evolving edited k-nearest neighbor classifiers.

The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate. In recent works, genetic algorithms have been successfully applied to determine which patterns must be included in the edited subset.