Advances in Wearable Photoplethysmography Applications in Health Monitoring

In the last few years, interest in wearable technology for physiological signal monitoring is rapidly growing, especially during and after the COVID-19 pandemic

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

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises specially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers

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

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

ADVANCES IN NONLINEAR HEART RATE VARIABILITY ANALYSIS FOR MOOD STATE ASSESSMENT AND EMOTION RECOGNITION

In this dissertation some advanced methodologies for the analysis of Heart Rate Variability (HRV) are proposed. This study is motivated by the necessity of improving the investigation of the autonomic control influence in the physiological aspects of mood assessment and affective computing. Several previous works pointed out the crucial role of the autonomic signals, in the assessment of health status. However many issues have arisen, due to the extraction of information from HRV in order to discern psychological states and emotions. At the same time, many evidences in the literature showed that physiological processes involve nonlinear frequency modulation or multi-feedback interactions associated to long-range correlations. For this reason many nonlinear parameters, extracted from autonomic signals, have already been used as markers of aging and presence of diseases. Throughout this thesis, existing nonlinear methodologies of analysis are improved and adapted to the study of long-term HRV recorded from bipolar patients, and novel approaches are proposed for the nonlinear analysis of the prompt response to emotion elicitation. In the first Chapter the literature about the control of Sympathetic and Parasympathetic Nervous Systems on the cardiovascular regulation is described, pointing out the difference between classical and nonlinear models. The second Chapter proposes a detailed review of standard and nonlinear methodologies for the analysis of HRV, describing time and frequency domains analysis, higher order spectra, time-frequency approaches and nonlinear methods derived from the phase-space theory. All the most powerful methods for the assessment of entropy in physiological signals are critically analyzed according to the changes between different algorithms. Statistical analysis and pattern recognition methods, used in to investigate the experimental data, are also reported in Chapter 2. The large majority of nonlinear techniques has to be applied to long-term recordings, in order to produce performing indexes. To overcome this limitation, which is especially relevant in the study of the immediate response to emotional stimuli, I discuss, in the Chapter 3, two main methods derived from the surface section of Poincaré, the Symbolic Analysis and the Lagged Poincaré Plot (LPP), proposing their use in the analysis of ultra-short time series (less than 5 minutes), through the study of novel parameters. The subjects recruitment and the experimental protocols are studied ad-hoc for each investigation and are described in Chapter 4. Three main objectives, according to the participants, can be identified: the discrimination of mood states in bipolar subjects, the characterization of psychological dimensions in non-pathological subjects and the emotion recognition in healthy subjects during protocols of affective computing. Wearable devices for the autonomic signals acquisition are used for the long-monitoring of patients affected by bipolar disease, in order to study the evolution of heartbeat dynamics while the subjects are involved in their every-day activities. In the emotion elicitation protocols, a multimodal stimulation is explored, through the research of standardized database of acoustic, tactile, olfactory and visual stimuli. In all the studies the Russel’s Circumplex Model of Affect (CMA) is used, detecting two dimensions in each emotions: the arousal (the level of intensity of the evoked emotion) and the valence (the pleasantness/unpleasantness). For visual and acoustic stimulation protocols, two existing databases of standardized stimuli, the International Affective Pictures System (IAPS) and International Affective Digitized Sound (IADS), are considered. The ratings of each sound and picture in this case is predetermined. In the other two cases, tactile and olfactory stimulation, the subjects are asked to assess their sensations through two scores of valence and arousal. In the affective touch protocol, caress-like stimuli, produced through an ad-hoc haptic device are elicited. The results of nonlinear methodologies application are presented in Chapter 5. A preliminary study about the reliability of LPP measures in ultra-short windows is described, through the analysis on synthetic series. The outcomes regarding bipolar patients are obtained through adapted versions of Multiscale Entropy (MSE) and Detrended Fluctuation Analysis (DFA). Ad-hoc algorithms of pattern recognition and prediction of the future mood state are developed using Markov model. A bivariate study of HRV and respiration signal of healthy subjects in resting state, through Multivariate Multiscale Entropy (MMSE), is applied to define the correlation between the complexity of autonomic dynamics and the psychological dimensions assessed through traditional questionnaires. Then the results of the validation of Symbolic Analysis and the LPP approaches to affective computing protocols are shown, using segments of signals of duration from thirty-five seconds to one minute and half