Reconsiderations about the clinical importance of the sympathetic skin response

Over the last few years, neurologists have been showing increasing interest in the study of the sympathetic skin response (SSR). In the present report we describe a simple method that permitted us to determine a wide variation of SSR in response to different stimuli such as respiration, deglutition, blinking, skeletal movements, biting, auditory or light stimuli, vocalization, and sphincter contraction. These results raise doubts about the role of SSR as a complementary diagnostic method.

Determination of the Maximum Rate of Eccrine Sweat Glands’ Ion Reabsorption Using the Galvanic Skin Conductance to Local Sweat Rate Relationship

Purpose The purpose of the present study was to develop and describe a simple method to evaluate the rate of ion reabsorption of eccrine sweat glands in human using the measurement of galvanic skin conductance (GSC) and local sweating rate (SR). This purpose was investigated by comparing the SR threshold for increasing GSC with following two criteria of sweat ion reabsorption in earlier studies such as 1) the SR threshold for increasing sweat ion was at approximately 0.2 to 0.5 mg/cm2/min and 2) exercise-heat acclimation improved the sweat ion reabsorption ability and would increase the criteria 1. Methods Seven healthy non-heat-acclimated male subjects received passive heat treatment both before and after 7 days of cycling in hot conditions (50% maximum oxygen uptake, 60 min/day, ambient temperature 32°C, and 50% relative humidity). Results Subjects became partially heat-acclimated, as evidenced by the decreased end-exercise heart rate (p<0.01), rate of perceived exhaustion (p<0.01), and oesophageal temperature (p=0.07), without alterations in whole-body sweat loss, from the first to the last day of training. As hypothesised, we confirmed that the SR threshold for increasing GSC was near the predicted SR during passive heating before exercise heat acclimation, and increased significantly after training (0.19 ± 0.09 to 0.32 ± 0.10 mg/cm2/min, p<0.05). Conclusions The reproducibility of sweat ion reabsorption by the eccrine glands in the present study suggests that the relationship between GSC and SR can serve as a new index for assessing the maximum rate of sweat ion reabsorption of eccrine sweat glands in humans.

Évaluation des jeux Kinect à l’aide du suivi physiologique, du suivi oculaire et des réactions faciales du joueur.

Les données sont analysées par le logiciel conçu par François Courtemanche et Féthi Guerdelli. L'expérimentation des jeux a eu lieu au Laboratoire de recherche en communication multimédia de l'Université de Montréal.

The use of wireless data communication and body sensing devices to evaluate occupants' comfort in buildings

Physiological parameters measured by an embedded body sensor system were demonstrated to respond to changes of the air temperature in an office environment. The thermal parameters were monitored with the use of a wireless sensor system that made possible to turn any existing room into a field laboratory. Two human subjects were monitored over daily activities and at various steady-state thermal conditions when the air temperature of the room was altered from 22-23°C to 25-28°C. The subjects indicated their thermal feeling on questionnaires. The measured skin temperature was distributed close to the calculated mean skin temperature corresponding to the given activity level. The variation of Galvanic Skin Response (GSR) reflected the evaporative heat loss through the body surfaces and indicated whether sweating occurred on the subjects. Further investigations are needed to fully evaluate the influence of thermal and other factors on the output given by the investigated body sensor system.

AN INVESTIGATION OF THE POSSIBLE RELATIONSHIP BETWEEN CERTAIN PHYSIOLOGICAL AND PSYCHOLOGICAL MEASURES AND SMOKING CESSATION IN A SELF - SELECTED POPULATION

This study focused on the possible relationship between certain physiological and psychological variables and the cessation of smoking. The population studied was employees enrolled in a multimodality smoking cessation program at the local offices of a major American corporation. In order to be eligible to participate, each individual must have become a non-smoker by the end of the smoking cessation program.^ Three physiological measures were taken on each individual while performing a relaxation exercise; (1) Electromyogram (EMG), (2) Galvanic Skin Response (GSR), and (3) Skin Temperature. The psychological measure consisted of the variable "anxiety" in the Cattell 16-PF personality inventory. Individual's self report of their smoking status was verified through a test for expired carbon monoxide levels.^ For the total population (N-31) no significant relationships were found between the physiological and psychological variable measured and cessation; however, with the removal of two cases discovered during the post-test interview to be influenced by external factors of high caffeine level and a severe family crisis, the measure of EMG, attained significance in discriminating between the successful and unsuccessful in Smoking Cessation. ^

Stress Detection by means of Stress Physiological Template

This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications

Secure access control by means of human stress detection

This paper proposes a stress detection system based on fuzzy logic and the physiological signals heart rate and galvanic skin response. The main contribution of this method relies on the creation of a stress template, collecting the behaviour of previous signals under situations with a different level of stress in each individual. The creation of this template provides an accuracy of 99.5% in stress detection, improving the results obtained by current pattern recognition techniques like GMM, k-NN, SVM or Fisher Linear Discriminant. In addition, this system can be embedded in security systems to detect critical situations in accesses as cross-border control. Furthermore, its applications can be extended to other fields as vehicle driver state-of-mind management, medicine or sport training.

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.

Design, implementation and evaluation of an unconstrained and contactless biometric system based on hand geometry and stress detection

Esta tesis propone un sistema biométrico de geometría de mano orientado a entornos sin contacto junto con un sistema de detección de estrés capaz de decir qué grado de estrés tiene una determinada persona en base a señales fisiológicas Con respecto al sistema biométrico, esta tesis contribuye con el diseño y la implementación de un sistema biométrico de geometría de mano, donde la adquisición se realiza sin ningún tipo de contacto, y el patrón del usuario se crea considerando únicamente datos del propio individuo. Además, esta tesis propone un algoritmo de segmentación multiescala para solucionar los problemas que conlleva la adquisición de manos en entornos reales. Por otro lado, respecto a la extracción de características y su posterior comparación esta tesis tiene una contribución específica, proponiendo esquemas adecuados para llevar a cabo tales tareas con un coste computacional bajo pero con una alta precisión en el reconocimiento de personas. Por último, este sistema es evaluado acorde a la norma estándar ISO/IEC 19795 considerando seis bases de datos públicas. En relación al método de detección de estrés, esta tesis propone un sistema basado en dos señales fisiológicas, concretamente la tasa cardiaca y la conductancia de la piel, así como la creación de un innovador patrón de estrés que recoge el comportamiento de ambas señales bajo las situaciones de estrés y no-estrés. Además, este sistema está basado en lógica difusa para decidir el grado de estrés de un individuo. En general, este sistema es capaz de detectar estrés de forma precisa y en tiempo real, proporcionando una solución adecuada para sistemas biométricos actuales, donde la aplicación del sistema de detección de estrés es directa para evitar situaciónes donde los individuos sean forzados a proporcionar sus datos biométricos. Finalmente, esta tesis incluye un estudio de aceptabilidad del usuario, donde se evalúa cuál es la aceptación del usuario con respecto a la técnica biométrica propuesta por un total de 250 usuarios. Además se incluye un prototipo implementado en un dispositivo móvil y su evaluación. ABSTRACT: This thesis proposes a hand biometric system oriented to unconstrained and contactless scenarios together with a stress detection method able to elucidate to what extent an individual is under stress based on physiological signals. Concerning the biometric system, this thesis contributes with the design and implementation of a hand-based biometric system, where the acquisition is carried out without contact and the template is created only requiring information from a single individual. In addition, this thesis proposes an algorithm based on multiscale aggregation in order to tackle with the problem of segmentation in real unconstrained environments. Furthermore, feature extraction and matching are also a specific contributions of this thesis, providing adequate schemes to carry out both actions with low computational cost but with certain recognition accuracy. Finally, this system is evaluated according to international standard ISO/IEC 19795 considering six public databases. In relation to the stress detection method, this thesis proposes a system based on two physiological signals, namely heart rate and galvanic skin response, with the creation of an innovative stress detection template which gathers the behaviour of both physiological signals under both stressing and non-stressing situations. Besides, this system is based on fuzzy logic to elucidate the level of stress of an individual. As an overview, this system is able to detect stress accurately and in real-time, providing an adequate solution for current biometric systems, where the application of a stress detection system is direct to avoid situations where individuals are forced to provide the biometric data. Finally, this thesis includes a user acceptability evaluation, where the acceptance of the proposed biometric technique is assessed by a total of 250 individuals. In addition, this thesis includes a mobile implementation prototype and its evaluation.

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.