Cosme Llerena

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.

Bioimpedance-Based Wearable Measurement Instrumentation for Studying the Autonomic Nerve System Response to Stressful Working Conditions

The assessment of mental stress on workers under hard and stressful conditions is critical to identify which workers are not ready to undertake a mission that might put in risk their own life and the life of others. The ATREC project aims to enable Real Time Assessment of Mental Stress of the Spanish Armed Forces during military activities. Integrating sensors with garments and using wearable measurement devices, the following physiological measurements were recorded: heart and respiration rate, skin galvanic response as well as peripheral temperature. The measuring garments are the following: a sensorized glove, an upper-arm strap and a repositionable textrode chest strap system with 6 textrodes. The implemented textile-enabled instrumentation contains: one skin galvanometer, two temperature sensors, for skin and environmental, and an Impedance Cardiographer/Pneumographer containing a 1 channel ECG amplifier to record cardiogenic biopotentials. The implemented wearable systems operated accordingly to the specifications and are ready to be used for the mental stress experiments that will be executed in the coming phases of the project in healthy volunteers.

Detection of Emotions and Stress through Speech Analysis

The term “stress” is referred to a psychological state, obtained in response to a situation which is accompanied by an emotional reaction, such as, for instance, anxiety, anger, sadness, etc. Emotions are psychophysiological reactions representing modes of adaptation to certain either environmental stimuli or own stimuli. They are those feelings or perceptions of the elements and relations of the reality and the imagination, which are physically expressed by some physiological functions, like, for example, facial reactions, changes in heartbeat or distortions in the paralinguistic aspects of speech. Although both stress and emotional state do not alter the linguistic content, they could affect the paralinguistic contents of speech and this is an important factor in human communication, because it provides more information about the interlocutor than the merely semantic one. This subliminal information of speech is analyzed in this paper. Throughout the present document many features and algorithms are studied, which are combined to obtain an emotion and stress detector with a high degree of reliability.

Synchronization for classical blind source separation algorithms in wireless acoustic sensor networks

The use of wireless acoustic sensor networks is becoming very popular since they entail many advantages. However, this type of distributed sensor networks has an important drawback for many signal processing algorithms, the synchronization problem. Broadly speaking, in those networks, signals received at the different nodes are not synchronized due to two main factors, the clock problem and the important differences in propagation delays between sources and microphones. In this work we introduce a synchronization solution for mixtures of two and three speech sources in the framework of blind source separation. This proposal of synchronization has a mixture alignment stage prior to apply the separation method. Obtained results demonstrate that this synchronization method aligns speech mixtures correctly since it improves the performance of the classical separation algorithm in terms of both speech quality and speech intelligibility.

Speech Enhancement in Noisy Environments in Hearing Aids Driven by a Tailored Gain Function Based on a Gaussian Mixture Model

This paper centers on a novel approach aiming at speech enhancement in hearing aids. It consists in creating -by making use of perceptual concepts, and a supervised learning process driven by a genetic algorithm (GA)- a gain function (\(\mathcal{G}\)) that not only does it enhance the speech quality but also the speech intelligibility in noisy environments. The proposed algorithm creates the enhanced gain function by using a Gaussian mixture model fueled by the GA. To what extent the speech quality is enhanced is quantitatively measured by the algorithm itself by using a scheme based on the perceptual evaluation of speech quality (PESQ) standard. In this “blind” process, it does not use any initial information but that iteratively quantified by the PESQ measurement. The GA computes the optimized parameters that maximize the PESQ score. The experimental work, carried out over three different databases, shows how the computed gain function assists the hearing aid in enhancing speech, when compared to the values reached by using a standard hearing aid based on a multiband compressor-expander algorithm.

Synchronizing Speech Mixtures in Speech Separation Problems under Reverberant Conditions

Blind Source Separation (BSS) techniques aim at recovering unobserved source signals from observed mixtures (typically, the outputs of an array of sensors). Practically all classical BSS techniques do not work properly under reverberant conditions and therefore, it still remains an open problem. In this sense, we propose in this document the use of synchronization of speech mixtures in order to improve the results of classical BSS techniques. Specifically, we have applied the synchronization of mixtures combined with one of the most well-known and robust BSS algorithms that works under non-reverberant conditions, the Degenerate Unmixing Estimation Technique (DUET). In the aim of synchronizing speech mixtures prior to the speech source separation, the suitability of working with seven Time Delay Estimation (TDE) techniques has been analyzed. Results show the feasibility of using synchronization since the results of DUET are improved and additionally, it has been observed what is the most useful TDE algorithm in this framework.

Feature Selection in Mental Stress Analysis using Multiple Biological Signals

Stress is a response of people to face up to daily mental, emotional or physical challenges. Continuous monitoring of stress levels of a subject is of key importance to understand and control personal stress. In this sense, different biological signals can be used, such as, heart rate (HR), respiration, galvanic skin response (GSR) or electric response of the muscles. In this paper we extract a large number of features from the aforementioned biological signals in order to classify the levels of stress. Once we calculate these features, we use a genetic algorithm combined with a least square linear discriminant (LSLD) in the aim of selecting the most suitable features, considering the error of classification. Results show that respiration is the most useful signal in the classification of stress level and specifically, entropy and recurrence analysis of that signal are the most relevant features. In the case of GSR, we observe that feet are more sensitive to changes of the electrodermal activity than hands. With respect to EMG, it is the less adequate signal to classify stress level.