Robust Multi-Scenario Speech-Based Emotion Recognition System.

Every human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech-words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task. Unfortunately, many of them were generated under non-real-world conditions, i.e., actors played emotions, and recorded emotions were under fictitious circumstances where noise is non-existent. Another weakness in the design of emotion recognition systems is the scarcity of enough patterns in the available databases, causing generalization problems and leading to overfitting. This paper examines how different recording environmental elements impact system performance using a simple logistic regression algorithm. Specifically, we conducted experiments simulating different scenarios, using different levels of Gaussian white noise, real-world noise, and reverberation. The results from this research show a performance deterioration in all scenarios, increasing the error probability from 25.57% to 79.13% in the worst case. Additionally, a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system are proposed. Our system's average error probability of 34.57% is comparable to the best-case scenario with 31.55%. The findings support the prediction that simulated emotional speech databases do not offer sufficient closeness to real scenarios.

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. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics.

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. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 seconds window length.

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. The main advantage of this technology is the absence of the need to have direct contact with the surface of the material under investigation, which must be a conductive one. Specifically interesting is the meander-line-coil based Lamb wave generation, since the directivity of the waves allows a study based in the circumferential wrap-around received signal. However, the variety of defect sizes changes the behavior of the signal when it passes through the pipeline. Because of that, it is necessary to apply advanced techniques based on Smart Sound Processing (SSP). These methods involve extracting useful information from the signals sensed with EMAT at different frequencies to obtain nonlinear estimations of the depth of the defect, and to select the features that better estimate the profile of the pipeline. The proposed technique has been tested using both simulated and real signals in steel pipelines, obtaining good results in terms of Root Mean Square Error (RMSE).

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. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

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. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.

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. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The 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.

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. In this paper we propose a novel implementation of a genetic algorithm for designing edited k-nearest neighbor classifiers. It includes the definition of a novel mean square error based fitness function, a novel clustered crossover technique, and the proposal of a fast smart mutation scheme. In order to evaluate the performance of the proposed method, results using the breast cancer database, the diabetes database and the letter recognition database from the UCI machine learning benchmark repository have been included. Both error rate and computational cost have been considered in the analysis. Obtained results show the improvement achieved by the proposed editing method.

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. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.

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. The proposed algorithm is based on a novel set of heuristically designed features inspired in the Mel frequency cepstral coefficients. Experiments carried out with real signals demonstrate that the three listening environments can be robustly classified with the proposed system, obtaining low error rates when using a small part of the total computational resources of the DSP of the device. This study demonstrates that the proposed system can be implemented with the available resources in state-of-the-art digital hearing aids.

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. Women were randomly assigned to receive true acupuncture or placebo acupuncture (also known as sham acupuncture). In the first group, a sterilized steel needle was inserted at the Ren Mai 6 point, which is located on the anterior midline between the umbilicus and the upper part of the pubic symphysis. In the second group, the insertion site was located at the same horizontal level as the Ren Mai 6 point but shifted slightly to the left of the anterior midline. The management of the third stage of labor was the same in both groups. RESULTS: Statistically significant differences were found, with an average time to placental expulsion of 15.2 minutes in the placebo group and 5.2 minutes in the acupuncture group. No major complications occurred in either group. DISCUSSION: These results confirm that acupuncture at the Ren Mai 6 point can decrease the time to placental expulsion. This treatment represents a simple, safe, and inexpensive way of decreasing the duration of the third stage of labor.

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. An accurate estimation of the Cole parameters is essential for the Body Composition Assessment (BCA) and the estimation process depends on several factors. One of them is the upper frequency limit used for the estimation and the other is the number of measured frequencies in the measurement frequency range. Both of them impose requirements on the measurement hardware, influencing largely in the complexity of the bioimpedance spectrometer. In this work an analysis of the error obtained when estimating the Cole parameters with several frequency ranges and different number of frequencies has been performed. The study has been done on synthetic EBIS data obtained from experimental Total Right Side (TRS) measurements. The results suggest that accurate estimations of R(0) and R(∞) for BCA measurements can be achieved using much narrower frequency ranges and quite fewer frequencies than electrical bioimpedance spectrometers commercially available nowadays do.

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. In this work, a new approach to estimate the Cole parameters from the real part of the admittance, i.e. the conductance, is presented and its performance is compared with the results produced with the traditional fitting of complex impedance to a depressed semi-circle. The obtained results prove that is feasible to obtain the full Cole equation from only the conductance data and also that the estimation process is safe from the influence capacitive leakage.

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. Furthermore, a novel fitting method done on the Impedance plane, without using any frequency information has been implemented and included in the comparison. Results show that the four methods perform relatively well but the best fitting in terms of Standard Error of Estimate is the fitting obtained from the resistance only. The results support the possibility of measuring only the resistive part of the bioimpedance to accurately fit Cole equation and estimate the Cole parameters, with entailed advantages.