Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings.

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement.

Multi-Method Technics and Deep Neural Networks Tools on Board ARGO USV for the Geoarchaeological and Geomorphological Mapping of Coastal Areas: The Case of Puteoli Roman Harbour.

The ARGO-USV (Unmanned Surface Vehicle for ARchaeological GeO-application) is a technological project involving a marine drone aimed at devising an innovative methodology for marine geological and geomorphological investigations in shallow areas, usually considered critical areas to be investigated, with the help of traditional vessels. The methodological approach proposed in this paper has been implemented according to a multimodal mapping technique involving the simultaneous and integrated use of both optical and geoacoustic sensors. This approach has been enriched by tools based on artificial intelligence (AI), specifically intended to be installed onboard the ARGO-USV, aimed at the automatic recognition of submerged targets and the physical characterization of the seabed. This technological project is composed of a main command and control system and a series of dedicated sub-systems successfully tested in different operational scenarios. The ARGO drone is capable of acquiring and storing a considerable amount of georeferenced data during surveys lasting a few hours. The transmission of all acquired data in broadcasting allows the cooperation of a multidisciplinary team of specialists able to analyze specific datasets in real time. These features, together with the use of deep-learning-based modules and special attention to green-compliant construction phases, are the particular aspects that make ARGO-USV a modern and innovative project, aiming to improve the knowledge of wide coastal areas while minimizing the impact on these environments. As a proof-of-concept, we present the extensive mapping and characterization of the seabed from a geoarchaeological survey of the underwater Roman harbor of Puteoli in the Gulf of Naples (Italy), demonstrating that deep learning techniques can work synergistically with seabed mapping methods.

Combining electrodermal activity analysis and dynamic causal modeling to investigate the visual-odor multimodal integration during face perception.

Objective. This study presents a novel methodological approach for incorporating information related to the peripheral sympathetic response into the investigation of neural dynamics. Particularly, we explore how hedonic contextual olfactory stimuli influence the processing of neutral faces in terms of sympathetic response, event-related potentials and effective connectivity analysis. The objective is to investigate how the emotional valence of odors influences the cortical connectivity underlying face processing and the role of face-induced sympathetic arousal in this visual-olfactory multimodal integration.Approach. To this aim, we combine electrodermal activity (EDA) analysis and dynamic causal modeling to examine changes in cortico-cortical interactions.Results. The results reveal that stimuli arising sympathetic EDA responses are associated with a more negative N170 amplitude, which may be a marker of heightened arousal in response to faces. Hedonic odors, on the other hand, lead to a more negative N1 component and a reduced the vertex positive potential when they are unpleasant or pleasant. Concerning connectivity, unpleasant odors strengthen the forward connection from the inferior temporal gyrus (ITG) to the middle temporal gyrus, which is involved in processing changeable facial features. Conversely, the occurrence of sympathetic responses after a stimulus is correlated with an inhibition of this same connection and an enhancement of the backward connection from ITG to the fusiform face gyrus.Significance. These findings suggest that unpleasant odors may enhance the interpretation of emotional expressions and mental states, while faces capable of eliciting sympathetic arousal prioritize identity processing.

Neuronal correlates of eyeblinks are an expression of primary consciousness phenomena.

The blinking rate far exceeds that required for moistening the cornea and changes depending on whether a person is resting or engaged in cognitive tasks. During ecological cognitive tasks (such as speaking, reading, and watching videos), blinks occur at breakpoints of attention suggesting a role in information segmentation, but the close relationship between cognition dynamics and blink timing still escapes a full understanding. The aim of the present study is to seek (1) if there is a temporal relationship between blink events and the consecutive steps of cognitive processing, and (2) if blink timing and the intensity of blink-related EEG responses are affected by task-relevance of stimuli. Our results show that, in a classical visual oddball task, (i) the occurrence of blinks is influenced by stimuli, irrespective of their relevance, (ii) blinks following relevant stimuli are only apparently delayed due to the need of finalizing a behavioural response, and (iii) stimulus relevance does not affect the intensity of the blink-related EEG response. This evidence reinforce the idea that blinks are not emitted until the last step of the processing sequence has been completed and suggests that blink-related EEG responses are generated by primary consciousness phenomena which are considered by their nature non-modulable (all-or-nothing) phenomena.

Autonomic Regulation of Facial Temperature during Stress: A Cross-Mapping Analysis.

Skin temperature reflects the Autonomic Nervous System (ANS)'s response to emotions and mental states and can be remotely measured using InfraRed Thermography. Understanding the physiological mechanisms that affect facial temperature is essential to improve the precision of emotional inference from thermal imaging. To achieve this aim, we recorded thermal images from 30 volunteers, at rest and under acute stress induced by the Stroop test, together with two autonomic correlates, i.e., heart rate variability and electrodermal activity, the former serving as a measure of cardiovascular dynamics, and the latter of the activity of the sweat glands. We used a Cross Mapping (CM) approach to quantify the nonlinear coupling of the temperature from four facial regions with the ANS correlates. CM reveals that facial temperature has a statistically significant correlation with the two autonomic time series, under both conditions, which was not evident in the linear domain. In particular, compared to the other regions, the nose shows a significantly higher link to the electrodermal activity in both conditions, and to the heart rate variability under stress. Moreover, the cardiovascular activity seems to be primarily responsible for the well-known decrease in nose temperature, and its coupling with the thermal signals significantly varies with gender.

Is Hypnotic Induction Necessary to Experience Hypnosis and Responsible for Changes in Brain Activity?

The relevance of formal hypnotic induction to the experience of trance and its neural correlates is not clear, in that hypnotizability, beliefs and expectation of hypnosis may play a major role. The aim of the study was assessing the EEG brain activity of participants with high (highs) or low hypnotizability scores (lows), aware of their hypnotizability level and informed that the session will include simple relaxation, formal hypnotic induction and neutral hypnosis. A total of 16 highs and 15 lows (according to the Stanford Hypnotic Susceptibility Scale, form A) were enrolled. Their EEGs were recorded during consecutive conditions of open/closed-eyes relaxation, hypnotic induction, neutral hypnosis and post hypnosis not interrupted by interviews. The studied variables were theta, alpha and gamma power spectral density (PSD), and the Determinism (DET) and Entropy (ENT) of the EEG signal Multidimensional Recurrence Plot (mRP). Highs reported significantly greater changes in their state of consciousness than lows across the session. The theta, alpha and gamma PSD did not exhibit condition-related changes in both groups. The Alpha PSD was larger in highs than in lows on midline sites, and the different sides/regions' theta and gamma PSD were observed in the two groups independently from conditions. ENT showed no correlation with hypnotizability, while DET positively correlated with hypnotizability during hypnosis. In conclusion, the relevance of formal hypnotic induction to the experience of trance may be scarce in highs, as they are aware of their hypnotizability scores and expecting hypnosis. Cognitive processing varies throughout the session depending on the hypnotizability level.

Modulation of the heartbeat evoked cortical potential by hypnotizability and hypnosis.

Hypnotizability is a psychophysiological trait measured by scales and associated with several differences, including interoceptive accuracy and the morpho-functional characteristics of interoception-related brain regions. The aim of the study was to assess whether the amplitude of the heartbeat evoked cortical potential (HEP), a correlate of interoceptive accuracy, differs in participants with low (lows) and high (highs) hypnotizability scores (assessed by the Stanford Hypnotic Susceptibility Scale, Form A) before and after the induction of hypnosis. ECG and EEG were monitored in 16 highs and 15 lows during an experimental session, including open eyes baseline (B), closed eyes relaxation (R), hypnotic induction (IND), neutral hypnosis (NH), and post session baseline (Post). No significant difference was observed between groups and conditions in autonomic variables. The HEP amplitude was lower in highs than in lows at the right parietal site, likely due to hypnotizability related differences in the functional connection between the right insula and parietal cortex. It increased in highs and decreased in lows across the session, possibly due to the highs' preeminently internally directed attention and to the lows' possible disengagement from the task. Since interoception is involved in several cognitive-emotional functions, its hypnotizability related differences may contribute to the variability of experience and behavior in daily life.

Donkey Heart Rate and Heart Rate Variability: A Scoping Review.

Heart rate (HR) and heart rate variability (HRV) are commonly used physiological measures in animals. While several studies exist on horse HRV, less information is available for donkeys. This scoping review aims to understand the extent and type of published evidence on donkey HR and HRV, their clinical and research applications, the devices used, and the analysis performed. Only quantitative primary studies published in English were considered. Four different databases were queried through the Web of Science platform, with additional evidence identified by citation chasing. After a two-stage screening phase, data were extracted considering study and population characteristics, information on HR/HRV analysis, and applications. The majority of the 87 included articles (about 80%) concerned a sample size of up to 20 individuals and were published since 2011 (about 65%). Forty-one articles employed an electronic device for signal acquisition (mainly electrocardiographs and heart rate monitors), yet only two articles reported HRV parameters. The literature on donkey HRV is lacking, and this gap can be filled by gaining knowledge on donkey characteristics and finding useful tools for welfare assessment. Comparison with what is known about the horse allows a discussion of the technical and interpretative difficulties that can be encountered with donkeys.

EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test.

Previous studies have identified several brain regions involved in the sympathetic response and its integration with pain, cognition, emotions and memory processes. However, little is known about how such regions dynamically interact during a sympathetic activation task. In this study, we analyzed EEG activity and effective connectivity during a cold pressor test (CPT). A source localization analysis identified a network of common active sources including the right precuneus (r-PCu), right and left precentral gyri (r-PCG, l-PCG), left premotor cortex (l-PMC) and left anterior cingulate cortex (l-ACC). We comprehensively analyzed the network dynamics by estimating power variation and causal interactions among the network regions through the direct directed transfer function (dDTF). A connectivity pattern dominated by interactions in [Formula: see text] (8-12) Hz band was observed in the resting state, with r-PCu acting as the main hub of information flow. After the CPT onset, we observed an abrupt suppression of such [Formula: see text]-band interactions, followed by a partial recovery towards the end of the task. On the other hand, an increase of [Formula: see text]-band (1-4) Hz interactions characterized the first part of CPT task. These results provide novel information on the brain dynamics induced by sympathetic stimuli. Our findings suggest that the observed suppression of [Formula: see text] and rise of [Formula: see text] dynamical interactions could reflect non-pain-specific arousal and attention-related response linked to stimulus' salience.

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.

The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification model, an interpretable machine learning model founded on the Bayesian nature of Gaussian Processes (GPs). Specifically, we inject problem-specific domain knowledge of inherent physiological mechanisms underlying the psycho-physiological states as a prior distribution over the GP latent space. Thus, to estimate the hyper-parameters in PhGP, we rely on the information from raw physiological signals as well as the designed prior function encoding the physiologically-inspired modelling assumptions. Alongside this new model, we present novel interpretability metrics that highlight the most informative input regions that contribute to the GP prediction. We evaluate the ability of PhGP to provide an accurate and interpretable classification on three different datasets, including electrodermal activity (EDA) signals collected during emotional, painful, and stressful tasks. Our results demonstrate that, for all three tasks, recognition performance is improved by using the PhGP model compared to competitive methods. Moreover, PhGP is able to provide physiological sound interpretations over its predictions.

Blood Analytes as Biomarkers of Mechanisms Involved in Alzheimer's Disease Progression.

Alzheimer's disease (AD) is the leading cause of dementia, but the pathogenetic factors are not yet well known, and the relationships between brain and systemic biochemical derangements and disease onset and progression are unclear. We aim to focus on blood biomarkers for an accurate prognosis of the disease. We used a dataset characterized by longitudinal findings collected over the past 10 years from 90 AD patients. The dataset included 277 observations (both clinical and biochemical ones, encompassing blood analytes encompassing routine profiles for different organs, together with immunoinflammatory and oxidative markers). Subjects were grouped into four severity classes according to the Clinical Dementia Rating (CDR) Scale: mild (CDR = 0.5 and CDR = 1), moderate (CDR = 2), severe (CDR = 3) and very severe (CDR = 4 and CDR = 5). Statistical models were used for the identification of potential blood markers of AD progression. Moreover, we employed the Pathfinder tool of the Reactome database to investigate the biological pathways in which the analytes of interest could be involved. Statistical results reveal an inverse significant relation between four analytes (high-density cholesterol, total cholesterol, iron and ferritin) with AD severity. In addition, the Reactome database suggests that such analytes could be involved in pathways that are altered in AD progression. Indeed, the identified blood markers include molecules that reflect the heterogeneous pathogenetic mechanisms of AD. The combination of such blood analytes might be an early indicator of AD progression and constitute useful therapeutic targets.

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.

Human body odors of happiness and fear modulate the late positive potential component during neutral face processing: a preliminary ERP study on healthy subjects.

Human body odors (HBOs) are powerful stimuli that can affect emotional, cognitive and behavioral processes. However, the characterization of the physiological response to HBOs is still to be fully investigated. Here, we analyzed the self-assessed emotion perception and the EEG event-related potentials (ERP) on 17 healthy young women during a simultaneous visual-olfactory stimulation. Particularly, we evaluated the effect of happiness and fear HBO on the amplitude of ERP waveforms elicited by neutral face processing. In addition, we evaluated the subjective valence and arousal perception of the presented neutral faces by means of the self-assessment-manikin test. We observed a significant increase in the amplitude of the late positive potential (LPP) for central left sites (i.e., C3) during the administration of HBOs with respect to clean air. On the other hand, we did not observe any significant change in the subjective valence and arousal scores as well as for the early components of the ERP (i.e., P100, N170, Vertex-Positive-Potential). Our preliminary results suggest that fear and happiness HBO can induce a protracted increase in the LPP, and possibly reflect an automatic and sustained engagement with emotionally significant content.

Modeling subjective fear using skin conductance: a preliminary study in virtual reality.

Reliably measuring fear perception could help evaluate the effectiveness of treatments for pathological conditions such as specific phobias or post-traumatic stress syndrome (e.g., exposure therapy). In this study, we developed a novel vir-tual reality (VR) scenario to induce fear and evaluate the related physiological response by the analysis of skin conductance (SC) signal. Eighteen subjects voluntarily experienced the fear VR scenario while their SC was recorded. After the experiment, each participant was asked to score the perceived subjective fear using a Likert scale from 1 to 10. We used the cvxEDA algorithm to process the collected SC signals and extract several features able to estimate the autonomic response to the fearful stimuli. Finally, the extracted features were linearly combined to model the subjective fear perception scores by means of LASSO linear regression. The sparsification imposed by the LASSO procedure to mitigate the overfitting risk identified an optimal linear model including only the standard deviation of the tonic SC component as a regressor (p = 0.007; R2 = 0.3337). The significant contribution of this feature to the model suggests that subjects experiencing more intense subjective fear have a more variable and unstable sympathetic tone.

Ground Control System for UAS Safe Landing Area Determination (SLAD) in Urban Air Mobility Operations.

The use of the Unmanned Aerial Vehicles (UAV) and Unmanned Aircraft System (UAS) for civil, scientific, and military operations, is constantly increasing, particularly in environments very dangerous or impossible for human actions. Many tasks are currently carried out in metropolitan areas, such as urban traffic monitoring, pollution and land monitoring, security surveillance, delivery of small packages, etc. Estimation of features around the flight path and surveillance of crowded areas, where there is a high number of vehicles and/or obstacles, are of extreme importance for typical UAS missions. Ensuring safety and efficiency during air traffic operations in a metropolitan area is one of the conditions for Urban Air Mobility (UAM) operations. This paper focuses on the development of a ground control system capable of monitoring crowded areas or impervious sites, identifying the UAV position and a safety area for vertical landing or take-off maneuvers (VTOL), ensuring a high level of accuracy and robustness, even without using GNSS-derived navigation information, and with on-board terrain hazard detection and avoidance (DAA) capabilities, in particular during operations conducted in BVLOS (Beyond Visual Line Of Sight). The system is composed by a mechanically rotating real-time LiDAR (Light Detection and Ranging) sensor, linked to a Raspberry Pi 3 as SBC (Session Board Controller), and interfaced to a GCS (Ground Control Station) by wireless connection for data management and 3-D information transfer.

Towards a Contactless Stress Classification Using Thermal Imaging.

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner.

Reliability of Pulse Rate Variability in Elderly Men and Women: an Application of Cross-Mapping Approach.

Photoplethysmography (PPG) is a completely noninvasive, optical method of assessing blood flow dynamics in peripheral vasculature. Wearable devices for PPG recording are becoming increasingly popular, due to their cost-effectiveness and ease of use. For these reasons, many recent scientific studies have proposed the use of pulse rate variability (PRV) extracted from PPG as a surrogate for heart rate variability (HRV), in monitoring autonomic activity and cardiovascular health.In this work, we used a cross-mapping approach, a methodology based on chaos theory, to compare PRV and HRV dynamics, and investigate their agreement according to age and gender of healthy subjects. We used ECG and PPG data acquired from 57 subjects (41 young and 16 elderly) during resting state in the supine position. Signals were gathered from the publicly available VORTAL dataset. Our results showed a statistically significant decrease of PRV reliability as an HRV surrogate in old participants, which was confirmed as significant when only men subjects were analyzed (p-value<0.01).Our findings, although preliminary, suggest greater caution in the use of PPG devices for monitoring cardiovascular health, especially in elderly men.

Discriminating Stress From Cognitive Load Using Contactless Thermal Imaging Devices.

This study proposes long wave infrared technology as a contactless alternative to wearable devices for stress detection. To this aim, we studied the change in facial thermal distribution of 17 healthy subjects in response to different stressors (Stroop Test, Mental Arithmetic Test). During the experimental sessions the electrodermal activity (EDA) and the facial thermal response were simultaneously recorded from each subject. It is well known from the literature that EDA can be considered a reliable marker for the psychological state variation, therefore we used it as a reference signal to validate the thermal results. Statistical analysis was performed to evaluate significant differences in the thermal features between stress and non-stress conditions, as well as between stress and cognitive load. Our results are in line with the outcomes of previous studies and show significant differences in the temperature trends over time between stress and resting conditions. As a new result, we found that the mean temperature changes of some less studied facial regions, e.g., the right cheek, are able not only to significantly discriminate between resting and stressful conditions, but also allow to recognize the typology of stressors. This outcome not only directs future studies to consider the thermal patterns of less explored facial regions as possible correlates of mental states, but more importantly it suggests that different psychological states could potentially be discriminated in a contactless manner.

Odor valence modulates cortico-cortical interactions: a preliminary study using DCM for EEG.

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity.