Emotions arise from a complex interplay of various factors, including conscious experience, physiological processes, and contextual elements. Although emotions are inherently dynamic processes, this aspect is oftentimes neglected in experimental protocols. In this study, we employed dynamical systems theory to investigate the time-varying self-assessed emotion ratings. We used the continuous ratings of the publicly available CASE dataset, in which thirty individuals rated their level of arousal and valence while watching videos designed to evoke four different emotions. Firstly, we analyzed the univariate dynamics by reconstructing the phase space from the arousal and valence series separately, and quantified their regularity and spatial complexity by using three metrics: Fuzzy, Sample, and Distribution Entropy. Then, we combined the arousal and valence series and proposed a novel index, the Multichannel Distribution Entropy (MDistEn), to estimate the complexity of the bivariate phase space. By coupling the two dimensions, we found that MDistEn resulted as an effective marker of fear, showing patterns statistically different from all of the other stimuli (p-value$\leq$0.001). These findings support the investigation of the time-varying dynamics of annotated emotion ratings as a promising pathway to discriminate the onset of fear-related pathological states.

Anxiety symptoms are important contributors to the global health-related burden. Low-intensity interventions have been proposed to reduce anxiety symptoms in the population. Among these, biofeedback (BF) offers an effective approach to reducing anxiety. In the present study, BF was integrated into a novel virtual reality (VR) architecture to enhance BF's effectiveness to i) evaluate the feasibility of a VR-based single-session BF in teaching participants to self-regulate; ii) compare the BF aiming at reducing sympathetic (measured though the tonic level of skin conductance, SCL) versus increasing cardiac vagal (i.e., normalized high frequency of heart rate variability, HFnu-HRV) activation, and iii) evaluate which of the two VR-BF single-sessions was most effective in reducing perceived state anxiety. 20 healthy participants underwent both SCL- and HFnu-based in a single session VR-BF. Results showed the feasibility of a short single-session VR-BF and the effectiveness of both VR-BF sessions in reducing perceived state anxiety. Moreover, SCL-based VR-BF determined a significant reduction in sympathetic activation and in sympathovagal balance as well as a greater reduction in perceived state anxiety compared to HFnu-based VR-BF. SCL-based VR-BF represents a safe and effective intervention in reducing anxiety while enhancing adaptive psychophysiological activation.

Objective: The use of commercially available optical-see-through (OST) head-mounted displays (HMDs) in their own peripersonal space leads the user to experience two perception conflicts that deteriorate their performance in precision manual tasks: the vergence-accommodation conflict (VAC) and the focus rivalry. In this work, we aim characterizing for the first time the psychophysiological response associated with user's incorrect focus cues during the execution of an augmented reality (AR)-guided manual task with the Microsoft HoloLens OST-HMD. Methods: 21 subjects underwent to a “connecting-the-dots” experiment with and without the use of AR, and in both binocular and monocular conditions. For each condition, we quantified the changes in autonomic nervous system (ANS) activity of subjects by analyzing the electrodermal activity (EDA) and heart rate variability. Moreover, we analyzed the neural central correlates by means of power measures of brain activity and multivariate autoregressive measures of brain connectivity extracted from the electroencephalogram (EEG). Results: No statistically significant differences of ANS correlates were observed among tasks, although all EDA-related features varied between rest and task conditions. Conversely, significant differences among conditions were present in terms of EEG-power variations in the$\mu$(8–13) Hz and$\beta$(13–30) Hz bands. In addition, significant changes in the causal interactions of a brain network involved in motor movement and eye-hand coordination comprising the precentral gyrus, the precuneus, and the fusiform gyrus were observed. Conclusion: The physiological plausibility of our results suggest promising future applicability to investigate more complex scenarios, such as AR-guided surgery.

Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62 percent. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00 percent. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.

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.

The investigation of the physiological and pathological processes involved in fear perception is complicated due to the difficulties in reliably eliciting and measuring the complex construct of fear. This study proposes a novel approach to induce and measure subjective fear and its physiological correlates combining virtual reality (VR) with a mixed-effects model based on skin conductance (SC). Specifically, we developed a new VR scenario applying specific guidelines derived from horror movies and video games. Such a VR environment was used to induce fear in eighteen volunteers in an experimental protocol, including two relaxation scenarios and a neutral virtual environment. The SC signal was acquired throughout the experiment, and after each virtual scenario, the emotional state and fear perception level were assessed using psychometric scales. We statistically evaluated the greatest sympathetic activation induced by the fearful scenario compared to the others, showing significant results for most SC-derived features. Finally, we developed a rigorous mixed-effects model to explain the perceived fear as a function of the SC features. Model-fitting results showed a significant relationship between the fear perception scores and a combination of features extracted from both fast- and slow-varying SC components, proposing a novel solution for a more objective fear assessment.

Emotion regulation to pleasant and unpleasant stimuli involves several brain areas, such as the prefrontal cortex, amygdala, and insular cortex. However, how a specific arousal level affects such brain dynamics is not fully understood. To this effect, we propose an electroencephalography (EEG)-based study, where 22 healthy subjects were emotionally elicited through affective pictures gathered from the International Affective Picture System. Based on the circumplex model of affect, we used four arousing levels, each with two valence levels (i.e., pleasant and unpleasant). Considering these levels, we investigated the EEG power spectra and functional connectivity among channels. We then used this information to build an automatic valence classifier. The experimental results showed that the functional connectivity at the highest frequency bands (i.e., > 30 Hz) was most sensitive to arousal modulation. Specifically, high connectivity over the right hemisphere occurred during pleasant elicitation, whereas that over the left hemisphere occurred during negative elicitation. In addition, short-range connections in the frontal regions became weaker with increased arousal level, whereas long-range connections were enhanced. Concerning the spectral analysis, the most significant valence-dependent changes were found at intermediate arousing elicitations over the prefrontal and occipital regions. The automatic valence classification showed a recognition accuracy of up to 86.37 percent.

This study reports on how velocity and force levels of caress-like haptic stimuli can elicit different emotional responses, which can be identified through the analysis of Autonomic Nervous System (ANS) dynamics. Affective stimuli were administered on the forearm of 32 healthy volunteers (16 women) through a haptic device with two levels of force, 2 N and 6 N, and two levels of velocity, 9.4 mm/s and 37 mm/s. ANS dynamics was estimated through Heart Rate Variability (HRV) linear and nonlinear analysis on recordings gathered before and after each stimulus. To this extent, we here propose and assess novel features from HRV symbolic analysis and Lagged Poincaré Plot. Classification was performed following a leave-one-subject-out procedure on nonlinear support vector machines. Pattern classification was split according to gender, significantly improving accuracies of recognition with respect to a “all-subjects” classification. Caressing force and velocity levels were recognized with up to 80 percent accuracy for men, and up to 84.38 percent for women. Our results demonstrate that changes in ANS control on cardiovascular dynamics, following emotional changes induced by caress-like haptic stimuli, can be effectively recognized by the proposed computational approach, considering that they occur in a gender-specific and nonlinear manner.

In this study, we investigated brain dynamics from electroencephalographic (EEG) signals during affective tactile stimulation conveyed by the dynamical contact with different fabrics. Thirty-three healthy subjects (16 females) were enrolled to interact with a haptic device able to mimic caress-like stimuli conveyed by strips of different fabrics moved back and forth at different velocities. Specifically, two velocity levels (i.e., 9.4 and 65 mm/sec) and two kinds of fabric (i.e., burlap and silk) were selected to deliver pleasant and unpleasant affective elicitations, according to subjects' self-assessment. EEG power spectra and functional connectivity were then calculated and analyzed. Experimental results, reported in terms of p-value topographic maps, demonstrated that caresses administered through unpleasant fabrics increased brain activity in the θ (4-8 Hz), α (8-14 Hz), and β (14-30 Hz) bands, whereas the use of pleasant fabrics enhanced functional connections in specific areas (e.g., frontal, occipital, and temporal cortices) depending on the oscillations frequency and caressing velocity. Furthermore, we adopted K-NN algorithms to automatically recognize the pleasantness of the haptic stimulation at a single-subject level using EEG power spectra, achieving a recognition accuracy up to 74.24%. Finally, we showed how brain oscillation power in the α and β bands over contralateral frontal- and central-cortex were the most informative features characterizing the pleasantness of a tactile stimulus on the forearm.

The number of studies about ultrashort cardiovascular time series is increasing because of the demand for mobile applications in telemedicine and e-health monitoring. However, the current literature still needs a proper validation of heartbeat nonlinear dynamics assessment from ultrashort time series. This paper reports on the reliability of the Lagged Poincaré Plot (LPP) parameters-calculated from ultrashort cardiovascular time series. Reliability is studied on simulated as well as on real RR series. Simulated RR series are generated and LPP parameters estimated for ultrashort time series (from 15 to 60 s) are compared to those estimated from 1 h. All LPP parameters estimated from time series longer than 35 s presented a Spearman's correlation coefficient higher than 0.99. RR series acquired from 32 healthy subjects during 5-min resting state sessions are used to test the LPP approach in experimental data. The usefulness of ultrashort term parameters in real data is accomplished also studying their ability to discriminate positive and negative valence of auditory stimuli taken from the International Affective Digitized Sound System (IADS) dataset. The achieved accuracies in the recognition of elicitation along the valence dimension, using only the LPP parameters, were of 77.78% for 1 min 28 s series, and of 79.17% for 35 s series.

We propose the use of the convex optimization-based EDA (cvxEDA) framework to automatically characterize the force and velocity of caressing stimuli through the analysis of the electrodermal activity (EDA). CvxEDA, in fact, solves a convex optimization problem that always guarantees the globally optimal solution. We show that this approach is especially suitable for the implementation in wearable monitoring systems, being more computationally efficient than a widely used EDA processing algorithm. In addition, it ensures low-memory consumption, due to a sparse representation of the EDA phasic components. EDA recordings were gathered from 32 healthy subjects (16 females) who participated in an experiment where a fabric-based wearable haptic system conveyed them caress-like stimuli by means of two motors. Six types of stimuli (combining three levels of velocity and two of force) were randomly administered over time. Performance was evaluated in terms of execution time of the algorithm, memory usage, and statistical significance in discerning the affective stimuli along force and velocity dimensions. Experimental results revealed good performance of cvxEDA model for all of the considered metrics.

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.

The papers in this special section focus on the topic of sensor informatics for mental health applications. The papers provide novel insights on advances in detection, sensing, analysis, and modeling of central and/or autonomic correlates useful in psychophysiological states assessment.

Bipolar Disorder (BD) is characterized by an alternation of mood states from depression to (hypo)mania. Mixed states, i.e., a combination of depression and mania symptoms at the same time, can also be present. The diagnosis of this disorder in the current clinical practice is based only on subjective interviews and questionnaires, while no reliable objective psychophysiological markers are available. Furthermore, there are no biological markers predicting BD outcomes, or providing information about the future clinical course of the phenomenon. To overcome this limitation, here we propose a methodology predicting mood changes in BD using heartbeat nonlinear dynamics exclusively, derived from the ECG. Mood changes are here intended as transitioning between two mental states: euthymic state (EUT), i.e., the good affective balance, and non-euthymic (non-EUT) states. Heart Rate Variability (HRV) series from 14 bipolar spectrum patients (age: 33.439.76, age range: 23-54; 6 females) involved in the European project PSYCHE, undergoing whole night ECG monitoring were analyzed. Data were gathered from a wearable system comprised of a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire ECGs. Each patient was monitored twice a week, for 14 weeks, being able to perform normal (unstructured) activities. From each acquisition, the longest artifact-free segment of heartbeat dynamics was selected for further analyses. Sub-segments of 5 minutes of this segment were used to estimate trends of HRV linear and nonlinear dynamics. Considering data from a current observation at day t0, and past observations at days (t􀀀1, t􀀀2,...,), personalized prediction accuracies in forecasting a mood state (EUT/non-EUT) at day t+1 were 69% on average, reaching values as high as 83.3%. This approach opens to the possibility of predicting mood states in bipolar patients through heartbeat nonlinear dynamics exclusively.

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.

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.

The analysis of cognitive and autonomic responses to emotionally relevant stimuli could provide a viable solution for the automatic recognition of different mood states, both in normal and pathological conditions. In this study, we present a methodological application describing a novel system based on wearable textile technology and instantaneous nonlinear heart rate variability assessment, able to characterize the autonomic status of bipolar patients by considering only electrocardiogram recordings. As a proof of this concept, our study presents results obtained from eight bipolar patients during their normal daily activities and being elicited according to a specific emotional protocol through the presentation of emotionally relevant pictures. Linear and nonlinear features were computed using a novel point-process-based nonlinear autoregressive integrative model and compared with traditional algorithmic methods. The estimated indices were used as the input of a multilayer perceptron to discriminate the depressive from the euthymic status. Results show that our system achieves much higher accuracy than the traditional techniques. Moreover, the inclusion of instantaneous higher order spectra features significantly improves the accuracy in successfully recognizing depression from euthymia.

This paper reports on the autonomic nervous system (ANS) changes and driving style modifications as a response to incremental stressing level stimulation during simulated driving. Fifteen subjects performed a driving simulation experiment consisting of three driving sessions. Starting from a first session where participants performed a steady motorway driving, the experimental protocol includes two additional driving sessions with incremental stress load. More specifically, the first stressing load consists of randomly administering mechanical stimuli to the vehicle during a steady motorway driving by means of a series of sudden and unexpected skids, such as those produced by a strong wind gust. These skids were supposed to produce in the driver a given level of stress. In order to assess this mental workload, dedicated psychological tests were performed. The second stressing load implied an incremental psychological load, consisting of a battery of time pressing arithmetical questions, added to the mechanical stimuli. For the whole experimental session, the driver's physiological signals and the vehicle's mechanical parameters were recorded and analyzed. In this paper, the ANS changes were investigated in terms of heart rate variability, respiration activity, and electrodermal response along with mechanical information such as that coming from steering wheel angle corrections, velocity changes, and time responses. Results are satisfactory and promising. In particular, significant statistical differences were found among the three driving sessions with an increasing stress level both in ANS responses and mechanical parameter changes. In addition, a good recognition of these sessions was carried out by pattern classification algorithms achieving an accuracy greater than 90%.

This paper reports on results of a pattern recognition technique for classifying pathological mental states of bipolar disorders using information gathered from the electrodermal response. The rationale behind this work is that the autonomic nervous system dynamics, non-invasively quantified through the electrodermal response processing, is altered by the specific mood state. Starting from the hypothesis that bipolar disorders are associated with affective dysfunctions, we processed data gathered from four bipolar patients through eleven experimental trials while an ad-hoc emotional stimulation is administered. Intra- and inter-subject variability were investigated. We show that, using a deconvolution-based approach to estimate sympathetic ANS markers and simple k-Nearest Neighbor algorithms, the proposed methodology is able to discern up to three mood states such as depression, hypo-mania, and euthymia with an average intra-subject accuracy greater than 98% and inter-subject accuracy greater than 82%.

Bipolar patients are characterized by a pathological unpredictable behavior, resulting in fluctuations between states of depression and episodes of mania or hypomania. In the current clinical practice, the psychiatric diagnosis is made through clinician-administered rating scales and questionnaires, disregarding the potential contribution provided by physiological signs. The aim of this paper is to investigate how changes in the autonomic nervous system activity can be correlated with clinical mood swings. More specifically, a group of ten bipolar patients underwent an emotional elicitation protocol to investigate the autonomic nervous system dynamics, through the electrodermal activity (EDA), among different mood states. In addition, a control group of ten healthy subjects were recruited and underwent the same protocol. Physiological signals were analyzed by applying the deconvolutive method to reconstruct EDA tonic and phasic components, from which several significant features were extracted to quantify the sympathetic activation. Experimental results performed on both the healthy subjects and the bipolar patients supported the hypothesis of a relationship between autonomic dysfunctions and pathological mood states.

Current clinical practice in diagnosing patients affected by psychiatric disorders such as bipolar disorder is based only on verbal interviews and scores from specific questionnaires, and no reliable and objective psycho-physiological markers are taken into account. In this paper, we propose to use a wearable system based on a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire electrocardiogram, respirogram, and body posture information in order to detect a pattern of objective physiological parameters to support diagnosis. Moreover, we implemented a novel ad hoc methodology of advanced biosignal processing able to effectively recognize four possible clinical mood states in bipolar patients (i.e., depression, mixed state, hypomania, and euthymia) continuously monitored up to 18 h, using heart rate variability information exclusively. Mood assessment is intended as an intrasubject evaluation in which the patient's states are modeled as a Markov chain, i.e., in the time domain, each mood state refers to the previous one. As validation, eight bipolar patients were monitored collecting and analyzing more than 400 h of autonomic and cardiovascular activity. Experimental results demonstrate that our novel concept of personalized and pervasive monitoring constitutes a viable and robust clinical decision support system for bipolar disorders recognizing mood states with a total classification accuracy up to 95.81%.

This paper reports on a new methodology for the automatic assessment of emotional responses. More specifically, emotions are elicited in agreement with a bidimensional spatial localization of affective states, that is, arousal and valence dimensions. A dedicated experimental protocol was designed and realized where specific affective states are suitably induced while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), ElectroDermal Response (EDR), and ReSPiration activity (RSP), are simultaneously acquired. A group of 35 volunteers was presented with sets of images gathered from the International Affective Picture System (IAPS) having five levels of arousal and five levels of valence, including a neutral reference level in both. Standard methods as well as nonlinear dynamic techniques were used to extract sets of features from the collected signals. The goal of this paper is to implement an automatic multiclass arousal/valence classifier comparing performance when extracted features from nonlinear methods are used as an alternative to standard features. Results show that, when nonlinearly extracted features are used, the percentages of successful recognition dramatically increase. A good recognition accuracy (>;90 percent) after 40-fold cross-validation steps for both arousal and valence classes was achieved by using the Quadratic Discriminant Classifier (QDC).

The objective of this study is to investigate the synchronization between breathing patterns and heart rate during emotional visual elicitation, that is, using sets of images gathered from the international affective picture system having five levels of arousal and five levels of valence, including a neutral reference level. Thirty-five healthy volunteers were emotionally elicited in agreement with a bidimensional spatial localization of affective states, i.e., arousal/valence plane, while two peripheral physiological signals, ECG and Respiration activity, were acquired simultaneously. The synchronization was then quantified by applying the concept of phase synchronization of chaotic oscillators, i.e., the cardio-respiratory synchrogram. This technique allowed us to estimate the synchronization ratio m:n as the attendance of n heartbeats in each m respiratory cycle, even for noisy and nonstationary data. We found a stronger evidence of cardiorespiratory synchronization during arousal than during neutral states.

Handshaking represents a complex motor and cognitive task that poses several challenges from both engineering and neuroscientific viewpoints. In particular, it is an intriguing application which can be profitably studied in the field of Human Robot Interaction (HRI). In this work an experimental paradigm is proposed to investigate the neural correlates of handshaking between humans and between humans and robots using functional Magnetic Resonance Imaging. More specifically the role of visual and haptic components during handshaking interaction will be studied. A wearable sensing glove will be used to monitor hand finger position and movement. Preliminary results will be reported and discussed.

The purpose of this study is to comparatively evaluate the performance of different wearable systems based on indirect breathing monitoring in terms of susceptibility to motion artifacts. These performances are compared with direct respiratory measurements using a spirometer, which is accurate, reliable, and less sensitive to movement artifacts, but cannot be integrated into truly wearable form. Experiments were carried out on four indirect methods implemented into wearable systems, inductive plethysmography, impedance plethysmography, piezoresistive pneumography, and piezoelectric pneumography, to ascertain the performance of each of them in terms of noise due to movement artifacts, as well as to study the effects of different movements or gestures during each test. A group of volunteers was asked to wear all of the breath monitoring systems simultaneously along with the face mask of the spirometer while carrying out four physical exercises in a gym under controlled conditions. Data are analyzed in the time and frequency domain to estimate the frequency respiration from each wearable system and compare it with those of the spirometer. Results confirmed that all the wearable systems are somehow affected by movement artifacts, but statistical investigation showed that for most of the physical exercises, three out of four, piezoelectric pneumography provided best performance in terms of robustness and reduced susceptibility to movement artifacts.

This paper is concerned with a new wearable system, which is able to monitor several vital signals and physiological variables in order to determine the cardiopulmonary activity status during emergencies. The innovative system consists of a multimodal broadband piezoelectric transducer based on polyvinylidene fluoride polymer integrated into a textile belt wrapped around the chest. An advanced electronic control unit, floating power supply, and wireless communication support make it suitable for portable monitoring during critical cardiopulmonary failures. The multimodal transducer is innovative in that only one sensitive element is employed to work as either an ultrasound (US) transceiver or piezoelectric sensor. The US transceiver is enabled to work at high frequency, i.e., it is excited by suitable pulses to emit an ultrasonic wave, which penetrates the body and receives the echo signals bouncing off the biological interfaces having different acoustic impedances. The piezoelectric sensor works at low frequency and acquires both signals generated by heart apex movements and the mechanical movement of the chest induced by respiration. This multimodality is allowed by a broadband of sensitivity jointly at a low value of the figure of merit (Q). Moreover, the transducer thickness is thin enough to assure a good adaptability to the biological site, and it is equipped with an advanced control unit enabling to switch from a high to a low working frequency. If jointly used along with an ECG wearable Holter, this transducer can be used to provide an exhaustive picture of the health status of the subject in the diagnostic and prognostic domains.

The aim of this paper is constituted by the feasibility study and development of a system based on Field Programmable Gate Array for the most significant cardiac arrhythmias recognition by means of Kohonen Self-Organizing Map. The feasibility study on an implementation on the XILINX Virtex®-4 FX12 FPGA is proposed, in which the QRS complexes are extracted and classified in real time between normal or pathologic classes. The whole digital implementation is validated to be integrated in wearable cardiac monitoring systems.

In this paper we investigate the possibility of processing the tactile perception by using a novel biomimetic approach for the pattern recognition module. The goal is to enhance the perception in complex virtual environments deriving from haptic displays mimicking human tactile discrimination. To do this we explored a Minimally Invasive Surgery application where the tactile information are strictly limited. In fact, this promising technique suffers from some evident limitations due to the surgeon loss of tactile perception during palpation of internal organs. This is basically due to the mechanical transmission of the elongated tools used during operation. We propose to integrate an Artificial Neural Network in an electronic board capable of processing data provided by a sensorized laparoscopic tool. The capabilities of several pattern recognition techniques present in literature, the Principal Component Analysis (PCA), a Multilayer Perceptron (MLP) and a Kohonen Self-Organising Map (KSOM) are investigated. The results are compared with that obtained psychophysically on five viscoelastic materials.

Functional brain exploration methodologies such as functional magnetic resonance imaging (fMRI) are critical tools to study perceptual and cognitive processes. In order to develop complex and well-controlled fMRI paradigms, researchers are interested in using active interfaces with electrically powered actuators and sensors. Due to the particularity of the MR environment, safety and compatibility criteria have to be strictly followed to avoid risks to the subject under test, the operators or the environment, as well as to prevent artifacts in the images. This paper describes the design of an fMRI compatible mechatronic interface based on MR compatibility tests of materials and actuators. In particular, a statistical test is introduced to evaluate the presence of artifacts in the image sequences that could negatively affect the fMRI studies. The device with two degrees of freedom, allowing one translation with position-feedback along a horizontal axis and one rotation about a vertical axis linked to the translation, was realized to investigate the brain mechanisms of dynamic tactile perception tasks. It can be used to move and orient various objects below the finger for controlled tactile stimulation. The MR compatibility of the complete interface is shown using the statistical test as well as a functional study with a human subject

In this paper we report on a new improved free-hand haptic interface based on magnetorheological fluids (MRFs). MRFs are smart materials which change their rheology according to an external magnetic field. The new architecture here proposed results from the development and improvement of earlier prototypes. The innovative idea behind this device is to allow subjects interacting directly with an object, whose rheology is rapidly and easily changeable, freely moving their hands without rigid mechanical linkages. Numerical advanced simulation tests using algorithms based on finite element methods have been implemented, in order to analyze and predict the spatial distribution of the magnetic field. A special focus was laid on investigating on how the magnetic filed profile is altered by the introduction of the hand. Possible solutions were proposed to overcome this perturbation. Finally some preliminary psychophysical tests in order to assess the performance of the device are reported and discussed

In this paper, we report on a new technology used to implement strain sensors to be integrated in usual garments. A particular conductive mixture based on commercial products is realized and directly spread over a piece of fabric, which shows, after the treatment, piezoresistive properties, i.e., a change in resistance when it is strained. This property is exploited to realize sensorized garments such as gloves, leotards, and seat covers capable of reconstructing and monitoring body shape, posture, and gesture. In general, this technology is a good candidate for adherent wearable systems with excellent mechanical coupling with body surface. Here, we mainly focused on a sensorized glove able to detect posture and movements of the fingers. It could be used in several fields of application. We report on experimental results of a sensorized glove used as movements recorder for rehabilitation therapies and medicine. Furthermore, we describe a dedicated methodology used to read the output sensors which allowed to avoid using metallic wires for the connections. The price to be paid for all these advantages is a nonlinear electric response of the fabric sensor and a too long settling time, that in principle, make these sensors not suitable for real-time applications. Here we propose a hardware and computational solution to overcome this limitation.

In the last few years, the smart textile area has become increasingly widespread, leading to developments in new wearable sensing systems. Truly wearable instrumented garments capable of recording behavioral and vital signals are crucial for several fields of application. Here we report on results of a careful characterization of the performance of innovative fabric sensors and electrodes able to acquire vital biomechanical and physiological signals, respectively. The sensing function of the fabric sensors relies upon newly developed strain sensors, based on rubber-carbon-coated threads, and mainly depends on the weaving topology, and the composition and deposition process of the conducting rubber-carbon mixture. Fabric sensors are used to acquire the respitrace (RT) and movement sensors (MS). Sensing features of electrodes, instead rely upon metal-based conductive threads, which are instrumental in detecting bioelectrical signals, such as electrocardiogram (ECG) and electromyogram (EMG). Fabric sensors have been tested during some specific tasks of breathing and movement activity, and results have been compared with the responses of a commercial piezoelectric sensor and an electrogoniometer, respectively. The performance of fabric electrodes has been investigated and compared with standard clinical electrodes.

In this paper we propose an innovative prototype of a haptic display for whole-hand immersive exploration. We envision a new concept of haptic display, the Haptic Black Box, which can be imagined as a box where the operator can poke his/her bare hand, and interact with the virtual object by freely moving the hand without mechanical constraints. In this way sensory receptors on the whole operator's hand would be excited, rather than restricting to just one or few fingertips or phalanges. To progress towards such a challenging goal, magnetorheological (MR) fluids represent a very interesting and completely innovative technology. These fluids are composed of micronsized, magnetizable particles immersed in a synthetic oil. Exposure to an external magnetic field induces in the fluid a change in rheological behaviour turning it into a near-solid in few milliseconds. By removing the magnetic field, the fluid quickly returns to its liquid state. We briefly report on the design of this device, describe psychophysical experiments to assess performance for softness and shape exploration, and report on the experimental results.

Many applications in teleoperation and virtual reality call for the implementation of effective means of displaying to the human operator information on the softness and other mechanical properties of objects being touched. The ability of humans to detect softness of different objects by tactual exploration is intimately related to both kinesthetic and cutaneous perception, and haptic displays should be designed so as to address such multimodal perceptual channel. In this paper, we investigate the possibility of surrogating detailed tactile information for softness discrimination, with information on the rate of spread of the contact area between the finger and the specimen as the contact force increases. Devices for implementing such a perceptual channel are described, and a practical application to a mini-invasive surgery tool is presented. Psychophysical test results are reported, validating the effectiveness and practicality of the proposed approach.

Many applications in teleoperation and virtual reality call for the implementation of effective means of displaying to the human operator information on the softness and other mechanical properties of objects being touched. The ability of humans to detect softness if different objects by tactual exploration is intimately related to both kinesthetic and cutaneous perception, and haptic displays should be designed so as to address such multimodal perceptual channel. In this paper we investigate the possibility of surrogating detailed tactile information for softness discrimination, with information on the rate of spread of the contact area between the finger and the specimen. Devices for implementing this new perceptual channel are described, and some preliminary psychophysical test results reported, validating the effectiveness and practicality of the proposed approach.

Nowadays, the surgeon who is using minimally invasive tools loses almost completely the haptic perception of the manipulated tissue. In particular, he or she loses the perception of the tissue elastic properties. It is possible to modify the actual mini-invasive surgical tools in such a way that they may give a reliable estimation of the manipulated tissue properties for recognition and characterization purpose. In this paper we present a first attempt to realize a prototype of sensor-based surgical tool using a modified commercial tool. Experimental tests have shown that using such a tool could enhance surgeon's haptic perception of the manipulated tissue.