ComEDA: A new tool for stress assessment based on electrodermal activity

Non-specific sympathetic arousal responses to different stressful elicitations can be easily recognized from the analysis of physiological signals. However, neural patterns of sympathetic arousal during physical and mental fatigue are clearly not unitary. In the context of physiological monitoring through wearable and non-invasive devices, electrodermal activity (EDA) is the most effective and widely used marker of sympathetic activation. This study presents ComEDA, a novel approach for the characterization of complex dynamics of EDA. ComEDA overcomes the methodological limitations related to the application of nonlinear analysis to EDA dynamics, is not parameter-sensitive and is suitable for the analysis of ultra-short time series. We validated the proposed algorithm using synthetic series of white noise and 1/f noise, varying the number of samples from 50 to 5000. By applying our approach, we were able to discriminate a statistically significant increase of complexity in the 1/f noise with respect to white noise, obtaining p-values in the range [4.35 × 10−6, 0.03] after the Mann–Whitney test. Then, we tested ComEDA on both EDA signal and its tonic and phasic components, acquired from healthy subjects during four experimental protocols: two inducing a sympathetic activation through physical efforts and two based on mentally stressful tasks. Results are encouraging and promising, outperforming state of the art metrics such as the Sample Entropy. ComEDA shows good performance not only in discriminating between stressful tasks and resting state (p-value < 0.01 after the Wilcoxon non-parametric statistical test applied to EDA signals of all the four datasets), but also in differentiating different trends of complexity of EDA dynamics when induced by physical and mental stressors. These findings suggest future applications to automatically detect and selectively identify threats due to overwhelming stress impacting both physical and mental health or in the field of telemedicine to monitor autonomic diseases correlated to atypical sympathetic activation. The Matlab code implementing the ComEDA algorithm is available online

Complexity index from a personalized wearable monitoring system for assessing remission in mental health

This study discusses a personalized wearable monitoring system, which provides information and communication technologies to patients with mental disorders and physicians managing such diseases. The system, hereinafter called the PSYCHE system, is mainly comprised of a comfortable t-shirt with embedded sensors, such as textile electrodes, to monitor electrocardiogram-heart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a smartphone-based interactive platform for electronic mood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions from a pathological mood state (HM, DP, or MX - where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients' clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders

Recognizing emotions induced by affective sounds through heart rate variability

This paper reports on how emotional states elicited by affective sounds can be effectively recognized by means of estimates of Autonomic Nervous System (ANS) dynamics. Specifically, emotional states are modeled as a combination of arousal and valence dimensions according to the well-known circumplex model of affect, whereas the ANS dynamics is estimated through standard and nonlinear analysis of Heart rate variability (HRV) exclusively, which is derived from the electrocardiogram (ECG). In addition, Lagged Poincaré Plots of the HRV series were also taken into account. The affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal (intensity) and two levels of valence (unpleasant and pleasant). A group of 27 healthy volunteers were administered with these standardized stimuli while ECG signals were continuously recorded. Then, those HRV features showing significant changes (p < 0.05 from statistical tests) between the arousal and valence dimensions were used as input of an automatic classification system for the recognition of the four classes of arousal and two classes of valence. Experimental results demonstrated that a quadratic discriminant classifier, tested through Leave-One-Subject-Out procedure, was able to achieve a recognition accuracy of 84.72 percent on the valence dimension, and 84.26 percent on the arousal dimension

On the Role of Affective Properties in Hedonic and Discriminant Haptic Systems

Common haptic devices are designed to effectively provide kinaesthetic and/or cutaneous discriminative inputs to the users by modulating some physical parameters. However, in addition to this behavior, haptic stimuli were proven to convey also affective inputs to the brain. Nevertheless, such affective properties of touch are often disregarded in the design (and consequent validation) of haptic displays. In this paper we present some preliminary experimental evidences about how emotional feelings, intrinsically present while interacting with tactile displays, can be assessed. We propose a methodology based on a bidimensional model of elicited emotions evaluated by means of simple psychometric tests and statistical inference. Specifically, affective dimensions are expressed in terms of arousal and valence, which are quantified through two simple one-question psychometric tests, whereas statistical inference is based on rank-based non-parametric tests. In this work we consider two types of haptic systems: (i) a softness display, FYD-2, which was designed to convey purely discriminative softness haptic stimuli and (ii) a system designed to convey affective caress-like stimuli (by regulating the velocity and the strength of the “caress”) on the user forearm. Gender differences were also considered. In both devices, the affective component clearly depends on the stimuli and it is gender-related. Finally, we discuss how such outcomes might be profitably used to guide the design and the usage of haptic devices, in order to take into account also the emotional component, thus improving system performance

Towards a Novel Generation of Haptic and Robotic Interfaces: Integrating Active Physiology in Human-Robot Interaction

Haptic interfaces are special robots that interact with people to convey touch-related information. In addition to such a discriminative aspect, touch is also a highly emotion-related sense. However, while a lot of effort has been spent to investigate the perceptual mechanisms of discriminative touch and to suitably replicate them through haptic systems in human robot interaction (HRI), there is still a lot of work to do in order to take into account also the emotional aspects of tactual experience (i.e., the so-called affective haptics), for a more naturalistic human-robot communication. In this paper, we report evidences on how a haptic device designed to convey caress-like stimuli can influence physiological measures related to the autonomous nervous system (ANS), which is intimately connected to evoked emotions in humans. Specifically, a discriminant role of electrodermal response and heart rate variability can be associated to two different caressing velocities, which can also be linked to two different levels of pleasantness. Finally, we discuss how the results from this study could be profitably employed and generalized to pave the path towards a novel generation of robotic devices for HRI

Design and preliminary affective characterization of a novel fabric-based tactile display

In this work we present a novel wearable haptic system based on an elastic fabric which can be moved forward and backward over the user forearm thus simulating a human caress. The system allows to control both the velocity of the “caress-like” movement, by regulating motor velocity, and the “strength of the caress”, by regulating motor positions and hence the force exerted by the fabric on the user forearm. Along with a description of the mechanical design and control of the system, we also report the preliminary results of psycho-physiological assessment tests performed by six healthy participants. Such an assessment is intended as a preliminary characterization of the device capability of eliciting tactually emotional states in humans using different combinations of velocity and caress strength. The emotional state is expressed in terms of arousal and valence. Moreover, the activation of the autonomic nervous system is also evaluated through the analysis of the electrodermal response (EDR). The main results reveal a statistically significant correlation between the perceived arousal level and the “strength of the caress” and between the perceived valence level and the “velocity of the caress”. Moreover, we found that phasic EDR is able to discern between pleasant and unpleasant stimuli. These preliminary results are very encouraging and confirm the effectiveness of this device in conveying emotional-like haptic stimuli in a controllable and wearable fashion

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions

Recognizing AR-guided manual tasks through autonomic nervous system correlates: A preliminary study

Optical see-through head-mounted displays (HMD) enable optical superposition of computer-generated virtual data onto the user's natural view of the real environment. This makes them the most suitable candidate to guide manual tasks, as for augmented reality (AR) guided surgery. However, most commercial systems have a single focal plane at around 2-3 m inducing 'vergence-accommodation conflict' and 'focal rivalry' when used to guide manual tasks. These phenomena can often cause visual fatigue and low performance. In this preliminary study, ten subjects performed a precision manual task in two conditions: with or without using the AR HMD. We demonstrated a significant deterioration of the performance using the AR-guided manual task. Moreover, we investigated the autonomic nervous system response through the analysis of the heart rate variability (HRV) and electrodermal activity (EDA) signals. We developed a pattern recognition system that was able to automatically recognize the two experimental conditions using only EDA and HRV data with an accuracy of 75%. Our learning algorithm highlighted two different physiological patterns combining parasympathetic and sympathetic informati

Linear and nonlinear quantitative EEG analysis during neutral hypnosis following an opened/closed eye paradigm

Hypnotic susceptibility is a major factor influencing the study of the neural correlates of hypnosis using EEG. In this context, while its effects on the response to hypnotic suggestions are undisputed, less attention has been paid to “neutral hypnosis” (i.e., the hypnotic condition in absence of suggestions). Furthermore, although an influence of opened and closed eye condition onto hypnotizability has been reported, a systematic investigation is still missing. Here, we analyzed EEG signals from 34 healthy subjects with low (LS), medium (MS), and (HS) hypnotic susceptibility using power spectral measures (i.e., TPSD, PSD) and Lempel-Ziv-Complexity (i.e., LZC, fLZC). Indeed, LZC was found to be more suitable than other complexity measures for EEG analysis, while it has been never used in the study of hypnosis. Accordingly, for each measure, we investigated within-group differences between rest and neutral hypnosis, and between opened-eye/closed-eye conditions under both rest and neutral hypnosis. Then, we evaluated between-group differences for each experimental condition. We observed that, while power estimates did not reveal notable differences between groups, LZC and fLZC were able to distinguish between HS, MS, and LS. In particular, we found a left frontal difference between HS and LS during closed-eye rest. Moreover, we observed a symmetric pattern distinguishing HS and LS during closed-eye hypnosis. Our results suggest that LZC is better capable of discriminating subjects with different hypnotic susceptibility, as compared to standard power analysis