Skills and Expertise
- PostDoc Position
- Innovative strategies for multi parametric monitoring aiming at identifying physio-pathological mental status
Jan 2003 - Dec 2006
- PhD Student
- Analysis and development of wearable systems at low and high frequency bandwidth based on ultrasound transducers for cardiopulmonary activity monitoring
Research Items (120)
- Jan 2019
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 mm/s 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 pvalue topographic maps, demonstrated that caresses administered through unpleasant fabrics increased brain activity in the theta (4-8 Hz), alpha (8-14 Hz), and beta (14-30 Hz) bands, whereas the use of pleasant fabrics enhanced functional connections in specific areas (e.g., frontal, occipital, 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 alpha and beta bands over contralateral frontal- and central-cortex were the most informative features characterizing the pleasantness of a tactile stimulus on the forearm.
- Jul 2018
- 40° International Engineering in Medicine and Biology Conference (EMBC)
This paper reports on a novel real time index designed to assess the quality of electrocardiographic (ECG) traces recorded in a group of five horses during a submaximal treadmill test procedure. During the experimental protocol two ECG monitoring systems were simultaneously applied to the animals. The first system was equipped with textile electrodes while the second one with standard red-dot electrodes. The procedure comprised four phases with an increased treadmill velocity, specifically, Walk 1, Trot 1, Trot 2 and Gallop. Three signal quality levels have been fixed according to the amount of noise present in the ECG trace: good (G), acceptable (A), and unacceptable (U). Moreover, a statistical comparison between textile and red-dot electrodes has been performed in terms of percentage of signal belonging to each class. Even if preliminary, results showed that in each experimental phase textile electrodes are more robust to movement artifacts with respect to the reddot showing a significant evidence of their better performance. These results enable to design robust wearable monitoring systems suitable to improve the quality of collected ECG, reducing the great amount of motion artifacts due to red-dot electrode application and leading to a more accurate diagnosis of high speed arrhythmias.
We examined the Autonomic Nervous System (ANS) activity of horses in response to human body odors (BOs) produced under happy and fear states. The ANS response of horses was analyzed in terms of Heart Rate Variability (HRV) features extracted in the frequency domain. Our results revealed that human BOs induce sympathetic and parasympathetic changes and stimulate horses emotionally, suggesting interspecies transfer of emotions via BOs. These preliminary findings open the way to measure changes in horse's ANS dynamics in response to human internal states via human BOs, and allow us to better understand unexpected animal behavior that could compromise human-horse interaction. Moreover, it becomes possible to design more effective strategies to manage animals across a range of situations in which a strict humananimal interaction is required, such as the well known Animal Assisted Therapy (AAT).
This paper reports on a multiclass arousal recognition system based on autonomic nervous system linear and nonlinear dynamics during affective visual elicitation. We propose a new hybrid method based on Lagged Poincaré Plot (LPP) and symbolic analysis, hereinafter called LPPsymb. This tool uses symbolic analysis to evaluate the irregularity of the trends of Lagged Poincaré Plot (LPP) quantifiers over the lags, and is here applied to investigate complex Heart Rate Variability (HRV) changes during emotion stimuli. In the experimental protocol 22 healthy subjects were elicited through a passive visualization of affective images gathered from the international affective picture system. LPPsymb and standard HRV analysis (defined in time and frequency domains) were applied to HRV series of one minute length. Then, an ad-hoc pattern recognition algorithm based on quadratic discriminant classifier was implemented and validated through a leave-onesubject-out procedure. The best performance of the proposed classification algorithm for recognizing the four classes of arousal was obtained using nine features comprising heartbeat complex dynamics, achieving an accuracy of 71.59%.
This study reports on the complexity modulation of heartbeat dynamics in patients affected by bipolar disorder. In particular, a multiscale entropy analysis was applied to the R-R interval series, that were derived from electrocardiographic (ECG) signals for a group of nineteen subjects comprised of eight patients and eleven healthy control subjects. They were monitored using a textile-based sensorized t-shirt during the day and overnight for a total of 47 diurnal and 27 nocturnal recordings. Patients showed three different mood states: depression, hypomania and euthymia. Results show a clear loss of complexity during depressive and hypomanic states as compared to euthymic and healthy control states. In addition, we observed that a more significant complexity modulation among healthy and pathological mood states occurs during the night. These findings suggest that bipolar disorder is associated with an enhanced sleep-related dysregulation of the Autonomic Nervous System (ANS) activity, and that heartbeat complex dynamics may serve as a viable marker of pathological conditions in mental health.
Individual animals vary in their behaviour and reactions to novel situations. These differences may extend to differences in cognition among individuals. We tested twenty-six horses for their ability to detour around symmetric and asymmetric obstacles. All of the animals were able to get around the barrier to reach a food target, but varied in their approach. Some horses moved slowly but were more accurate in choosing the shortest way. Other horses acted quickly, consistently detoured in the same direction, and did not reliably choose the shortest way. The remaining horses shifted from a faster, directionally consistent response with the symmetric barrier, to a slower but more accurate response with the asymmetric barrier. The asymmetric barrier induced a reduction in heart rate variability, suggesting that this is a more demanding task. The different approaches used to solve the asymmetric task may reflect distinct cognitive styles in horses, which vary among individuals, and could be linked to different personality traits. Understanding equine behaviour and cognition can inform horse welfare and management.
- Aug 2017
- Complexity and Nonlinearity in Cardiovascular Signals
Nonlinear digital signal processing methods addressing system complexity have provided useful computational tools for helping in the diagnosis and treatment monitoring of a wide range of pathologies. In particular, heartbeat complexity measures have been successful in characterizing patients with mental disorders such as Major Depression and Bipolar Disorder. In this chapter, we describe the use of standard complexity measures such as sample entropy and multiscale entropy, as well as instantaneous measures of entropy to characterize pathological mood states when patients undergo affective elicitation or long-term monitoring. Results demonstrate that complexity measures of cardiovascular dynamics can be promising and viable tools to support clinical decision in mental health, improving on the diagnosis and management of psychiatric disorders.
Complex heartbeat dynamics is known to reflect subject's emotional state, thanks to numerous links to brain cortical and subcortical regions. Likewise, specific brain regions are deeply involved in vagally-mediated emotional processing and regulation. Nevertheless, although the brain-heart interplay has been studied during visual emotion elicitation, directional interactions have not been investigated so far. To fill this gap, in this study we investigate brain-heart dynamics during emotional elicitation in healthy subjects through measures of Granger causality (GC) between the two physiological systems. Data were gathered from 22 healthy volunteers who underwent pleasant/ unpleasant affective elicitation using pictures from the International Affective Picture System. Neutral emotional stimuli were elicited as well. High density electroencephalogram (EEG) signals were processed to obtain time-varying maps of cortical activation, whereas the associated instantaneous cardiovascular dynamics was estimated through inhomogeneous point-process models. Concerning the information transfer brain-to-heart, GE highlighted significant valence-dependent lateralization with respect to resting states. Furthermore, as a proof of concept, the study of heart-to-brain dynamics considering EEG oscillations in the γ band (30-45 Hz) highlighted differential information transfer between neutral and positive elicitations directed to the prefrontal cortex.
This paper reports on a novel method for the analysis of Heart Rate Variability (HRV) through Lagged Poincaré Plot (LPP) theory. Specifically a hybrid method, LPPsymb, including LPP quantifiers and related symbolic dynamics was proposed. LPP has been applied to investigate the autonomic response to pleasant and unpleasant pictures extracted from the International Affective Picture System (IAPS). IAPS pictures are standardized in terms of level of arousal, i.e. the intensity of the evoked emotion, and valence, i.e. the level of pleasantness/unpleasantness, according to the Circumplex model of Affects (CMA). Twenty-two healthy subjects were enrolled in the experiment, which comprised four sessions with increasing arousal level. Within each session valence increased from positive to negative. An ad-hoc pattern recognition algorithm using a Leave-One-Subject-Out (LOSO) procedure based on a Quadratic Discriminant Classifier (QDC) was implemented. Our pattern recognition system was able to classify pleasant and unpleasant sessions with an accuracy of 71.59%. Therefore, we can suggest the use of the LPPsymb for emotion recognition.
This study focuses on the analysis of human-horse dynamic interaction using cardiovascular information exclusively. Specifically, the Information Theoretic Learning (ITL) approach has been applied to a Human-Horse Interaction paradigm, therefore accounting for the nonlinear information of the heart-heart interplay between humans and horses. Heartbeat dynamics was gathered from humans and horses during three experimental conditions: absence of interaction, visual-olfactory interaction, and brooming. Cross Information Potential, Cross Correntropy, and Correntropy Coefficient were computed to quantitatively estimate nonlinear coupling in a group of eleven subjects and one horse. Results showed a statistical significant difference on all of the three interaction phases. Furthermore, a Support Vector Machine classifier recognized the three conditions with an accuracy of 90:9%. These preliminary and encouraging results suggest that ITL analysis provides viable metrics for the quantitative evaluation of human-horse interaction.
This study proposes a novel approach to measure the contractile force of eye blink that is generally obtained from the orbicularis oculi activity through Ocular ElectroMyo-Graphy (O-EMG). Here, O-EMG is compared with the eye information acquired through a wearable head-mounted eye-tracking system in order to investigate the possibility of using the eye-tracking in place of the O-EMG. Eight subjects were simultaneously monitored through an O-EMG and the eye-tracker while they were performing a structured protocol implying a variation in the blink contractile strength. Results showed that eye-tracking features were able to statistically discriminate three kinds of contractile forces similarly to EMG features. The consequent correlation analysis revealed that all the EMG-related features were significantly correlated with the eye-tracking ones with a p-value <;10-6. Moreover, considering the extracted eye-tracking features, i.e. Integrated Gaze Path (IGP) and Eye-closed Duration (ECD), IGP reported a higher Spearman's correlation values with eye-blink reflex magnitude (EBM) than ECD. These encouraging results suggest that the ocular information extracted from the eye-tracking could be profitably used in non-invasive ecological environments where wearability and comfortability play a crucial role in detecting spontaneous response.
- Nov 2016
Objectives: Recent research indicates that Heart Rate Variability (HRV) is affected in Bipolar Disorders (BD) patients. To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course, we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state. Methods: Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project. We extracted time-domain, frequency-domain and non-linear HRV-features and trained a Support Vector Machine (SVM) to classify HRV-features according to mood state. To evaluate the influence of adjacent mood states, we trained SVM with different HRV-feature sets: 1) belonging to each mood state considered alone; 2) belonging to each mood state and normalized using information from the preceding mood state; 3) belonging to each mood state and normalized using information from the preceding and subsequent mood states; 4) belonging to each mood state and normalized using information from two randomly chosen states. Results: SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered. Conclusions: Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones. Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients.
- Oct 2016
This paper focuses on the validation of smart textile electrodes used to acquire ECG signals in horses in a comfortable and robust manner. The performance of smart textile electrodes is compared with standard Ag/AgCl electrodes in terms of the percentage of Motion Artifacts (MAs, the noise that results from the movement of electrodes against the skin) and signal quality. Seven healthy Standardbred mares were equipped with two identical electronic systems for the simultaneous collection of ECGs. One system was equipped with smart textile electrodes, while the second was equipped with standard Ag/AgCl electrodes. Each horse was then monitored individually in a stall for one hour, without any movement constraints. The ECGs were visually examined by an expert who blindly labeled the ECG segments that had been corrupted by MAs. Finally, the percentage of MAs (MA%) was computed as the number of samples of the corrupted segments over the whole length of the signal. The total MA% was found to be lower for the smart textiles than for the Ag/AgCl electrodes. Consistent results were also obtained by investigating MAs over time. These results suggest that smart textile electrodes are more reliable when recording artefact-free ECGs in horses at rest. Thus, improving the acquisition of important physiological information related to the activity of the Autonomic Nervous System, such as Heart Rate Variability, could help to provide reliable information on the mood and state of arousal of horses.
This study reports on a preliminary estimation of the human-horse interaction through the analysis of the heart rate variability (HRV) in both human and animal by using the dynamic time warping (DTW) algorithm. Here, we present a wearable system for HRV monitoring in horses. Specifically, we first present a validation of a wearable electrocardiographic (ECG) monitoring system for horses in terms of comfort and robustness, then we introduce a preliminary objective estimation of the human-horse interaction. The performance of the proposed wearable system for horses was compared with a standard system in terms of movement artifact (MA) percentage. Seven healthy horses were monitored without any movement constraints. As a result, the lower amount of MA% of the wearable system suggests that it could be profitably used for reliable measurement of physiological parameters related to the autonomic nervous system (ANS) activity in horses, such as the HRV. Human-horse interaction estimation was achieved through the analysis of their HRV time series. Specifically, DTW was applied to estimate dynamic coupling between human and horse in a group of fourteen human subjects and one horse. Moreover, a support vector machine (SVM) classifier was able to recognize the three classes of interaction with an accuracy greater than 78%. Preliminary significant results showed the discrimination of three distinct real human-animal interaction levels. These results open the measurement and characterization of the already empirically-proven relationship between human and horse.
The electrodermal activity (EDA) is a reliable physiological signal for monitoring the sympathetic nervous system. Several studies have demonstrated that EDA can be a source of effective markers for the assessment of emotional states in humans. There are two main methods for measuring EDA: Endosomatic (internal electrical source) and exosomatic (external electrical source). Even though the exosomatic approach is the most widely used, differences between alternating current (AC) and direct current (DC) methods and their implication in the emotional assessment field have not yet been deeply investigated. This paper aims at investigating how the admittance contribution of EDA, studied at different frequency sources, affects the EDA statistical power in inferring on the subject's arousing level (neutral or aroused). To this extent, 40 healthy subjects underwent visual affective elicitations, including neutral and arousing levels, while EDA was gathered through DC and AC sources from 0 to 1 kHz. Results concern the accuracy of an automatic, EDA feature-based arousal recognition system for each frequency source. We show how the frequency of the external electrical source affects the accuracy of arousal recognition. This suggests a role of skin susceptance in the study of affective stimuli through electrodermal response.
We present a study focused on a quantitative estimation of a human-horse dynamic interaction. A set of measures based on magnitude and phase coupling between heartbeat dynamics of both humans and horses in three different conditions is reported: no interaction, visual/olfactory interaction and grooming. Specifically, Magnitude Squared Coherence (MSC), Mean Phase Coherence (MPC) and Dynamic Time Warping (DTW) have been used as estimators of the amount of coupling between human and horse through the analysis of their heart rate variability (HRV) time series in a group of eleven human subjects, and one horse. The rationale behind this study is that the interaction of two complex biological systems go towards a coupling process whose dynamical evolution is modulated by the kind and time duration of the interaction itself. We achieved a congruent and consistent statistical significant difference for all of the three indices. Moreover, a Nearest Mean Classifier was able to recognize the three classes of interaction with an accuracy greater than 70%. Although preliminary, these encouraging results allow a discrimination of three distinct phases in a real human-animal interaction opening to the characterization of the empirically proven relationship between human and horse.
This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.
This study presents a machine learning approach applied to ElectroEnchephaloGraphic (EEG) response in a group of subjects when exposed to a controlled olfactory stimulation experiment. In the literature, in fact, there are controversial results on EEG response to odorants. This study proposes a robust leave-one-subject-out classification method to recognize features extracted from EEG signals belonging to pleasant or unpleasant olfactory stimulation classes. An accuracy of 75% has been achieved in a group of 32 subjects. Moreover a set of features extracted from lateral electrodes emphasized that right and left hemispheres behave differently when the subjects are exposed to pleasant or unpleasant odours stimuli.
This study reports on the development of a gender-specific classification system able to discern between two valence levels of smell, through information gathered from electrodermal activity (EDA) dynamics. Specifically, two odorants were administered to 32 healthy volunteers (16 males) while monitoring EDA. CvxEDA model was used to process the EDA signal and extract features from both tonic and phasic components. The feature set was used as input to a K-NN classifier implementing a leave-one-subject-out procedure. Results show strong differences in the accuracy of valence recognition between men (62.5%) and women (78%). We can conclude that affective olfactory stimulation significantly affect EDA dynamics with a highly specific gender dependency.
Common haptic devices are designed to effectively provide kinaesthetic and/or cutaneous discriminative inputs to the users by modulating some physical parameters. However, in addition to this behavior, haptic stimuli were proven to convey also affective inputs to the brain. Nevertheless, such affective properties of touch are often disregarded in the design (and consequent validation) of haptic displays. In this paper we present some preliminary experimental evidences about how emotional feelings, intrinsically present while interacting with tactile displays, can be assessed. We propose a methodology based on a bidimensional model of elicited emotions evaluated by means of simple psychometric tests and statistical inference. Specifically, affective dimensions are expressed in terms of arousal and valence, which are quantified through two simple one-question psychometric tests, whereas statistical inference is based on rank-based non-parametric tests. In this work we consider two types of haptic systems: (i) a softness display, FYD-2, which was designed to convey purely discriminative softness haptic stimuli and (ii) a system designed to convey affective caress-like stimuli (by regulating the velocity and the strength of the “caress”) on the user forearm. Gender differences were also considered. In both devices, the affective component clearly depends on the stimuli and it is gender-related. Finally, we discuss how such outcomes might be profitably used to guide the design and the usage of haptic devices, in order to take into account also the emotional component, thus improving system performance.
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 psycho-physiological 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: 33.43 ± 9.76, age range: 23-54; six females) involved in the European project PSYCHE, undergoing whole night electrocardiogram (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 min 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.
Emotion perception, occurring in brain areas such as the prefrontal cortex and amygdala, involves autonomic responses affecting cardiovascular dynamics. However, how such brain-heart dynamics is further modulated by emotional valence (pleasantness/unpleasantness), also considering different arousing levels (the intensity of the emotional stimuli), is still unknown. To this extent, we combined electroencephalographic (EEG) dynamics and instantaneous heart rate estimates to study emotional processing in healthy subjects. Twenty-two healthy volunteers were elicited through affective pictures gathered from the International Affective Picture System. The experimental protocol foresaw 110 pictures, each of which lasted 10 s, associated to 25 different combinations of arousal and valence levels, including neutral elicitations. EEG data were processed using short-time Fourier transforms to obtain timevarying maps of cortical activation, whereas the associated instantaneous cardiovascular dynamics was estimated in the time and frequency domains through inhomogeneous point-process models. Brain-heart linear and nonlinear coupling was estimated through the maximal information coefficient (MIC). Considering EEG oscillations in the θ band (4-8 Hz), MIC highlighted significant arousal-dependent changes between positive and negative stimuli, especially occurring at intermediate arousing levels through the prefrontal cortex interplay. Moreover, high arousing elicitations seem to mitigate changes in brain-heart dynamics in response to pleasant/unpleasant visual elicitation. © 2016 The Author(s) Published by the Royal Society. All rights reserved.
This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. EDA can be considered one of the most common observation channels of sympathetic nervous system activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization and sparsity. The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation. Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.
This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p-value < 0.0.5).
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 Poincare 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.
- Oct 2015
- 2015 AEIT International Annual Conference (AEIT)
Emotion regulation involves several brain areas such as prefrontal cortex, amygdala, and insular cortex. However, how such a brain dynamics is further modulated by emotional valence (pleasantness /unpleasantness), considering also different arousing levels, i.e., the intensity of the emotional stimuli, is still unknown. To this extent, we here study Electroencephalographic (EEG) oscillatory dynamics in healthy subjects during emotional visual elicitation. Twenty-two healthy volunteers were elicited through affective pictures gathered from the International Affective Picture System (IAPS). The experimental protocol foresaw 110 images associated to 4 increasing arousing levels, each of which included two valence levels: pleasant and unpleasant. EEG spectral analysis revealed no significant changes, between the two valence levels, during the lower and higher arousing elicitation at all considered bands, 6, a, ft, and 7. On the other hand, valence changes in the intermediate arousing sessions, in the 6, ft and 7 bands, were associated to changes in the prefrontal and parietal regions.
Bipolar disorder is characterized by mood swings alternating from depression to (hypo-)manic, including mixed states. Currently, patient mood is typically assessed by clinician-administered rating scales and subjective evaluations exclusively. To overcome this limitation, here we propose a methodology predicting mood changes using heartbeat nonlinear dynamics. Such changes are intended as transitioning between euthymic state (EUT), i.e., the good affective balance, and non-euthymic state. We analyzed Heart Rate Variability (HRV) series gathered from four bipolar patients involved in the European project PSYCHE, undergoing 24h ECG monitoring through textile-based wearable systems. 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 74.18% on average. This approach is intended as a proof of concept of the possibility of predicting mood states in bipolar patients through heartbeat nonlinear dynamics exclusively.
The combination of persistent polycythemia with decreased physical and cognitive performances while living in high-altitude is defined as ‘Chronic Mountain Sickness’ (CMS). To date, the role of the autonomic nervous system in CMS is still unknown. In this study, we analyze instantaneous tracking of cardiovascular complexity in order to increase the knowledge on CMS physio-pathology. To this extent, we processed heartbeat dynamics gathered from 13 CMS males and 7 high-altitude male dwellers, taken as healthy controls (HC), during semi-supine bicycle tasks performed at 25W, 50W, and 100W, along with a recovery session. Instantaneous Dominant Lyapunov exponents (IDLE), as estimated through point-process nonlinear models with Laguerre and Volterra expansions, were evaluated from such series. Results showed that instantaneous heartbeat complex dynamics was significantly altered in CMS. In particular, IDLE increases were associated to CMS, with respect to HC, during the 25W and 50W exercise sessions (p < 0.01). Conversely, no statistical differences were found when analyzing the 100W session, and first recovery after exercise.
This study reports on the development of a gender-specific classification system able to discern between two levels of velocity of a caress-like stimulus, through information gathered from Autonomic Nervous System (ANS) linear and nonlinear dynamics. Specifically, caress-like stimuli were administered to 32 healthy volunteers (16 males) while monitoring electrocardiogram signal to extract Heart Rate Variability (HRV) series. Caressing stimuli were administered to the forearm at a fixed force level (6 N) and two levels of velocity, 9.4 mm/s and 37 mm/s. Standard HRV measures, defined in the time and frequency domain, as well as HRV nonlinear measures were extracted during the pre- and post-stimulus sessions, and given as an input to a Support Vector Machine (SVM) classifier implementing a leave-one-subject-out procedure. Results show an accuracy of velocity recognition of 70% for the men, and 84.38% for the women, when both standard and nonlinear HRV measures were taken into account. Conversely, non-significant results were achieved considering standard measures only, or a gender-aspecific classification. We can conclude that caress-like stimuli elicitation significantly affect HRV nonlinear dynamics with a highly specific gender dependency.
This study reports on the implementation of a novel system to detect and reduce movement artifact (MA) contribution in electrocardiogram (ECG) recordings acquired from horses in free movement conditions. The system comprises both integrated textile electrodes for ECG acquisition and one triaxial accelerometer for movement monitoring. Here, ECG and physical activity are continuously acquired from seven horses through the wearable system and a model that integrates cardiovascular and movement information to estimate the MA contribution is implemented. Moreover, in this study we propose a new algorithm where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG recodigns. Achieved results showed a reduction of MA percentage greater than 40% between before- and after the application of the proposed algorithm to seven hours of recordings.
This study reports on the implementation of a novel system to detect and reduce movement artifact (MA) contribution in electrocardiogram (ECG) recordings acquired from horses in free movement conditions. The system comprises both integrated textile electrodes for ECG acquisition and one triaxial accelerometer for movement monitoring. Here, ECG and physical activity are continuously acquired from seven horses through the wearable system and a model that integrates cardiovascular and movement information to estimate the MA contribution is implemented. Moreover, in this study we propose a new algorithm where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG recodigns. Achieved results showed a reduction of MA percentage greater than 40% between before-and after-the application of the proposed algorithm to seven hours of recordings.
This paper investigates how the autonomic nervous system dynamics, quantified through the analysis of the electrodermal activity (EDA), is modulated according to affective haptic stimuli. Specifically, a haptic display able to convey caress-like stimuli is presented to 32 healthy subjects (16 female). Each stimulus is changed according to six combinations of three velocities and two forces levels of two motors stretching a strip of fabric. Subjects were also asked to score each stimulus in terms of arousal (high/low activation) and valence (pleasant/unpleasant), in agreement with the circumplex model of affect. EDA was processed using a deconvolutive method, separating tonic and phasic components. A statistical analysis was performed in order to identify significant differences in EDA features among force and velocity levels, as well as in their valence and arousal scores. Results show that the simulated caress induced by the haptic display significantly affects the EDA. In detail, the phasic component seems to be inversely related to the valence score. This finding is new and promising, since it can be used, e.g., as an additional cue for haptics design.
Non-verbal signals expressed through body language play a crucial role in multi-modal human communication during social relations. Indeed, in all cultures facial expressions are the most universal and direct signs to express innate emotional cues. A human face conveys important information in social interactions and helps us to better understand our social partners and establish empathic links. Latest researches show that humanoid and social robots are becoming increasingly similar to humans, both aesthetically and expressively. However, their visual expressiveness is a crucial issue that must be improved to make these robots more realistic and intuitively perceivable by humans as not different from them. This study concerns the capability of a humanoid robot to exhibit emotion through facial expressions. More specifically, emotional signs performed by a humanoid robot have been compared with corresponding human facial expressions in terms of recognition rate and response time. The set of stimuli included standardized human expressions taken from an Ekman-based database and the same facial expressions performed by the robot. Furthermore, participants' psychophysiological responses have been explored to investigate whether there could be differences induced by interpreting robot or human emotional stimuli. Preliminary results show a trend to better recognize expressions performed by the robot than 2D photos or 3D models. Moreover no significant differences in the subjects' psychophysiological state have been found during the discrimination of facial expressions performed by the robot in comparison with the same task performed with 2D photos and 3D models.
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.
- Jan 2015
In this work, a new head mounted eye tracking system is presented. Based on computer vision techniques, the system integrates eye images and head movement, in real time, performing a robust gaze point tracking. Nystagmus movements due to vestibulo-ocular reflex are monitored and integrated. The system proposed here is a strongly improved version of a previous platform called HATCAM, which was robust against changes of illumination conditions. The new version, called HAT-Move, is equipped with accurate inertial motion unit to detect the head movement enabling eye gaze even in dynamical conditions. HAT-Move performance is investigated in a group of healthy subjects in both static and dynamic conditions, i.e. when head is kept still or free to move. Evaluation was performed in terms of amplitude of the angular error between the real coordinates of the fixed points and those computed by the system in two experimental setups, specifically, in laboratory settings and in a 3D virtual reality (VR) scenario. The achieved results showed that HAT-Move is able to achieve eye gaze angular error of about 1 degree along both horizontal and vertical directions.
- Nov 2014
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.
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 tri-axial accelerometers for activity recognition. Moreover, on the patient-side, 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 hours 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 decisionin order to improve the diagnosis and management of psychiatric disorders.
Compared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.
This paper reports on a novel model based on convex optimization methods for the analysis of the skin conductance (SC) as response of the electrodermal activity (EDA) to affective stimuli. Starting from previous assessed methodological approaches, this new model proposes a decomposition of SC into tonic and phasic components through the solution of a convex optimization problem. Previous knowledge about the physiology of the EDA is accounted for by means of an appropriate choice of constraints and regularizers. In order to test the effectiveness of the new approach, an experimental session in which 9 healthy subjects were stimulated using affective pictures gathered from the IAPS database was designed and carried out. The experimental session included series of negative-valence high-arousal images and series of neutral images, with an inter-stimulus interval of about 2 seconds for both neutral and high arousal pictures. Next, a statistical analysis was performed on a set of features extracted from the phasic driver and the tonic signal estimated by the model. Results showed that the phasic driver extracted from the model was able to strongly distinguish arousal sessions from neutral ones. Conversely, no significant difference was found for the tonic components. This experimental findings are consistent with the literature and confirm that the phasic component is strictly related to changes in the sympathetic activity of the autonomic nervous system. Although preliminary, these results are very encouraging and future work will progress to further validate the model through specific and controlled experiments.
Complexity measures from Multiscale Entropy (MSE) analysis of cardiovascular variability may provide potential biomarkers of pathological mental states such as major depression. To this extent, in this study we investigate whether complexity of Heart Rate Variability (HRV) is also affected in mental disorders such as bipolar disorders (BD). As part of the European project PSYCHE, eight BD patients experiencing multiple pathological mood states among depression, hypomania, and euthymia (i.e., good affective balance) underwent long-term night recordings through a comfortable sensing t-shirt with integrated fabric electrodes and sensors. Standard radius, i.e., 20% of the HRV standard deviation, and a maximal-radius choice for the sample entropy estimation were compared along with a further multiscale Renyi Entropy analysis. We found that, despite the inter-subject variability, the maximal-radius MSE analysis is able to discern the considered pathological mental states of BD. As the current clinical practice in diagnosing BD is only based on verbal interviews and scores from specific questionnaires, these findings provide evidence on the possibility of using heartbeat complexity as the basis of novel clinical biomarkers of mental disorders.
Bipolar disorder is a chronic psychiatric condition during which patients experience mood swings among depression, hypomania or mania, mixed state (depression-hypomania) and euthymia, i.e., good affective balance. Nowadays, an objective characterization of the temporal trends of the disease as a response to the pharmacological treatment through physiological signatures, especially during severe episodes, is still missing. In this study we show interesting findings relating neuro-autonomic complexity to severe pathological mood states. More specifically, we studied Sample Entropy (SampEn) measures on Heart Rate Variability series gathered from four bipolar patients recruited within the frame of the European project PSYCHE. Patients were monitored through long term ECG recordings from the first hospital admission until clinical remission, i.e., the euthymic state. We observed that a mood transition from mixed-state to euthymia passing through depression can be characterized by increased SampEn values, i.e. as the patient is going to recover, SampEn increases. These results are in agreement with the current literature reporting on the complexity dynamics of the cardiovascular system and can provide a promising and viable clinical decision support to objectify the diagnosis and improve the management of psychiatric disorders.
- Jul 2014
This paper demonstrates that heartbeat complex dynamics is modulated by different pathological mental states. Multiscale entropy analysis was performed on R-R interval series gathered from the electrocardiogram of eight bipolar patients who exhibited mood states among depression, hypomania, and euthymia, i.e., good affective balance. Three different methodologies for the choice of the sample entropy radius value were also compared. We show that the complexity level can be used as a marker of mental states being able to discriminate among the three pathological mood states, suggesting to use heartbeat complexity as a more objective clinical biomarker for mental disorders.
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". Objectives: The goal of this work is to apply a computational methodology able to characterize mood states in bipolar patients through instantaneous analysis of heartbeat dynamics. Methods: A Point-Process-based Nonlinear Autoregressive Integrative (NARI) model is applied to analyze data collected from five bipolar patients (two males and three females, age 42.4 ± 10.5 range 32 -56) undergoing a dedicated affective elicitation protocol using images from the International Affective Picture System (IAPS) and Thematic Apperception Test (TAT). The study was designed within the European project PSYCHE (Personalised monitoring SYstems for Care in mental HEalth). Results: RESULTS demonstrate that the inclusion of instantaneous higher order spectral (HOS) features estimated from the NARI nonlinear assessment significantly improves the accuracy in successfully recognizing specific mood states such as euthymia and depression with respect to results using only linear indices. In particular, a specificity of 74.44% using the instantaneous linear features set, and 99.56% using also the nonlinear feature set were achieved. Moreover, IAPS emotional elicitation resulted in a more discriminant procedure with respect to the TAT elicitation protocol. Conclusions: A significant pattern of instantaneous heartbeat features was found in depressive and euthymic states despite the inter-subject variability. The presented point-process Heart Rate Variability (HRV) nonlinear methodology provides a promising application in the field of mood assessment in bipolar patients.
Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.
- May 2014
Mental disorders, characterized by impaired emotional and mood balance, are common in the West. Recent surveys have found that millions of people (age 18?65) have experienced some kind of mental disorder, such as psychotic disorder, major depression, bipolar disorder, panic disorder, social phobia, and somatoform disorder . Specifically, in 2010, 164.8 million people in Europe were affected by such illnesses .
- May 2014
- ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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%.
- May 2014
- 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
This study reports on a Multiscale Entropy (MSE) analysis on Heart Rate Variability (HRV) series gathered from eight patients with pathological mental states. Specifically, we found that an HRV complexity modulation exists in bipolar patients who exhibit mood states among depression, hypoma-nia, and euthymia, i.e., good affective balance. Two different methodologies for the choice of the sample entropy radius value were also compared. MSE analysis was performed on long-term night recordings acquired using a comfortable sensing t-shirt with integrated fabric electrodes and sensors developed in the frame of the European project PSYCHE. As the current clinical practice in diagnosing patients affected by psychiatric disorders such as bipolar disorder is only based on verbal interviews and scores from specific questionnaires, these findings increase the reliability of using heartbeat complexity as a more objective clinical biomarkers for bipolar disorders.
- May 2014
- 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
- Conference of the European Study Group on Cardiovascular oscillations
The objective quantification of psychological traits through Autonomic Nervous System (ANS) biomarkers is of large interest for the clinical and bioengineering scientific communities. Standard procedures, in fact, rely only on subjective scores from interviews and questionnaires. Here we show that monovariate and multivariate multiscale entropy measures are able to characterize the dimension regulating the behavior towards something pleasant in subjects. Such a psychological dimension was quantified through the BIS/BAS scores and investigated with a complexity analysis of Heart Rate Variability and respiratory activity. Results gathered from 29 volunteers show a significant decrease of multiscale complexity in subjects with higher behavioral activation score during the resting condition, supporting the significant role of ANS nonlinear dynamics in cognitive regulation processes.
The development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIM-engine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems.
- Feb 2014
- 2014 IEEE Haptics Symposium (HAPTICS)
In this work we present a novel wearable haptic system based on an elastic fabric which can be moved forward and backward over the user forearm thus simulating a human caress. The system allows to control both the velocity of the “caress-like” movement, by regulating motor velocity, and the “strength of the caress”, by regulating motor positions and hence the force exerted by the fabric on the user forearm. Along with a description of the mechanical design and control of the system, we also report the preliminary results of psycho-physiological assessment tests performed by six healthy participants. Such an assessment is intended as a preliminary characterization of the device capability of eliciting tactually emotional states in humans using different combinations of velocity and caress strength. The emotional state is expressed in terms of arousal and valence. Moreover, the activation of the autonomic nervous system is also evaluated through the analysis of the electrodermal response (EDR). The main results reveal a statistically significant correlation between the perceived arousal level and the “strength of the caress” and between the perceived valence level and the “velocity of the caress”. Moreover, we found that phasic EDR is able to discern between pleasant and unpleasant stimuli. These preliminary results are very encouraging and confirm the effectiveness of this device in conveying emotional-like haptic stimuli in a controllable and wearable fashion.
One of the main challenges in the study of human behavior is to quantitatively assess the participants’ affective states by measuring their psychophysiological signals in ecologically valid conditions. The quality of the acquired data, in fact, is often poor due to artifacts generated by natural interactions such as full body movements and gestures. We created a technology to address this problem. We enhanced the eXperience Induction Machine (XIM), an immersive space we built to conduct experiments on human behavior, with unobtrusive wearable sensors that measure electrocardiogram, breathing rate and electrodermal response. We conducted an empirical validation where participants wearing these sensors were free to move in the XIM space while exposed to a series of visual stimuli taken from the International Affective Picture System (IAPS). Our main result consists in the quantitative estimation of the arousal range of the affective stimuli through the analysis of participants’ psychophysiological states. Taken together, our findings show that the XIM constitutes a novel tool to study human behavior in life-like conditions.
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 a preliminary study aiming at investigating the eye gaze pattern and pupil size variation to discriminate emotional states induced by looking at pictures having different arousal content. A wearable and wireless eye gaze tracking system, hereinafter called HATCAM, which was able to robustly detect eye tracking and pupil area was used. A group of ten volunteers was presented with a set of neutral and arousal pictures extracted from the International Affective Picture System according to an ad-hoc experimental protocol. A set of features was extracted from eye gaze patterns and pupil size variations and used to classify the two classes of pictures. Although preliminary, results are very promising for affective computing applications.
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 hours, using heart rate variability information exclusively. Mood assessment is intended as an intra-subject 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 hours 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%.
We live in an era of data deluge and this requires novel tools to effectively extract, analyze and understand the massive amounts of data produced by the study of natural and artificial phenomena in many areas of research. We built a mixed reality system that uses multi-modal input and output and permits embodied interaction with large datasets. One of the applications of our system is in the exploration of the human brain connectome: the network of nodes and connections that defines the key information flow in the brain. With our system the user can be fully immersed in this complex data seeking to understand their dynamics and to discover new patterns.
- Jul 2013
- ACM SIGGRAPH 2013 Posters
We live in an era of data deluge and this requires novel tools to effectively extract, analyze and understand the massive amounts of data produced by the study of natural and artificial phenomena in many areas of research.
Emotion recognition based on autonomic nervous system signs is one of the ambitious goals of affective computing. It is well-accepted that standard signal processing techniques require relative long-time series of multivariate records to ensure reliability and robustness of recognition and classification algorithms. In this work, we present a novel methodology able to assess cardiovascular dynamics during short-time (i.e. < 10 seconds) affective stimuli, thus overcoming some of the limitations of current emotion recognition approaches. We developed a personalized, fully parametric probabilistic framework based on point-process theory where heartbeat events are modelled using a 2(nd)-order nonlinear autoregressive integrative structure in order to achieve effective performances in short-time affective assessment. Experimental results show a comprehensive emotional characterization of 4 subjects undergoing a passive affective elicitation using a sequence of standardized images gathered from the international affective picture system. Each picture was identified by the IAPS arousal and valence scores as well as by a self-reported emotional label associating a subjective positive or negative emotion. Results show a clear classification of two defined levels of arousal, valence and self-emotional state using features coming from the instantaneous spectrum and bispectrum of the considered RR intervals, reaching up to 90% recognition accuracy.
In this paper a novel and efficient computational implementation of a Spiking Neuron-Astrocyte Network (SNAN) is reported. Neurons are modeled according to the Izhikevich formulation and the neuron-astrocyte interactions are intended as tripartite synapsis and modeled with the previously proposed nonlinear transistor-like model. Concerning the learning rules, the original spike-timing dependent plasticity is used for the neural part of the SNAN whereas an ad-hoc rule is proposed for the astrocyte part. SNAN performances are compared with a standard spiking neural network (SNN) and evaluated using the polychronization concept, i.e., number of co-existing groups that spontaneously generate patterns of polychronous activity. The astrocyte-neuron ratio is the biologically inspired value of 1.5. The proposed SNAN shows higher number of polychronous groups than SNN, remarkably achieved for the whole duration of simulation (24 hours).
Mobilization and postural changes of patients with cognitive impairment are standard clinical practices useful for both psychic and physical rehabilitation process. During this process, several physiological signals, such as Electroen-cephalogram (EEG), Electrocardiogram (ECG), Photopletysmography (PPG), Respiration activity (RESP), Electrodermal activity (EDA), are monitored and processed. In this paper we investigated how quantitative EEG (qEEG) changes with postural modifications in minimally conscious state patients. This study is quite novel and no similar experimental data can be found in the current literature, therefore, although results are very encouraging, a quantitative analysis of the cortical area activated in such postural changes still needs to be deeply investigated. More specifically, this paper shows EEG power spectra and brain symmetry index modifications during a verticalization procedure, from 0 to 60 degrees, of three patients in Minimally Consciousness State (MCS) with focused region of impairment. Experimental results show a significant increase of the power in β band (12 - 30 Hz), commonly associated to human alertness process, thus suggesting that mobilization and postural changes can have beneficial effects in MCS patients.
This work aims at showing improved performances of an emotion recognition system embedding information gathered from cardiorespiratory (CR) coupling. Here, we propose a novel methodology able to robustly identify up to 25 regions of a two-dimensional space model, namely the well-known circumplex model of affect (CMA). The novelty of embedding CR coupling information in an autonomic nervous system-based feature space better reveals the sympathetic activations upon emotional stimuli. A CR synchrogram analysis was used to quantify such a coupling in terms of number of heartbeats per respiratory period. Physiological data were gathered from 35 healthy subjects emotionally elicited by means of affective pictures of the international affective picture system database. In this study, we finely detected five levels of arousal and five levels of valence as well as the neutral state, whose combinations were used for identifying 25 different affective states in the CMA plane. We show that the inclusion of the bivariate CR measures in a previously developed system based only on monovariate measures of heart rate variability, respiration dynamics and electrodermal response dramatically increases the recognition accuracy of a quadratic discriminant classifier, obtaining more than 90% of correct classification per class. Finally, we propose a comprehensive description of the CR coupling during sympathetic elicitation adapting an existing theoretical nonlinear model with external driving. The theoretical idea behind this model is that the CR system is comprised of weakly coupled self-sustained oscillators that, when exposed to an external perturbation (i.e. sympathetic activity), becomes synchronized and less sensible to input variations. Given the demonstrated role of the CR coupling, this model can constitute a general tool which is easily embedded in other model-based emotion recognition systems.
- Mar 2013
- Proceedings of the 4th Augmented Human International Conference
Today's increasingly large and complex databases require novel and machine aided ways of exploring data. To optimize the selection and presentation of data, we suggest an unconventional approach. Instead of exclusively relying on explicit user input to specify relevant information or to navigate through a data space, we exploit the power and potential of the users' unconscious processes in addition. To this end, the user is immersed in a mixed reality environment while his bodily reactions are captured using unobtrusive wearable devices. The users' reactions are analyzed in real-time and mapped onto higher-level psychological states, such as surprise or boredom, in order to trigger appropriate system responses that direct the users' attention to areas of potential interest in the visualizations. The realization of such a close experience-based human-machine loop raises a number of technical challenges, such as the real-time interpretation of psychological user states. The paper at hand describes a sensing architecture for empathetic data systems that has been developed as part of such a loop and how it tackles the diverse challenges.
- Jan 2013
- ACM SIGGRAPH 2013 Posters
We live in an era of data deluge and this requires novel tools to ef- fectively extract, analyze and understand the massive amounts of data produced by the study of natural and artificial phenomena in many areas of research. We built a mixed reality system that uses multi-modal input and output and permits embodied interaction with large datasets. One of the applications of our system is in the exploration of the human brain connectome: the network of nodes and connections that defines the key information flow in the brain. With our system the user can be fully immersed in this complex data seeking to understand their dynamics and to discover new patterns.
- Sep 2012
- Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom)
Autism Spectrum Disorder (ASD) is a neural development disorder characterized by specific patterns of behavioral and social difficulties. Beyond these core symptoms, additional problems such as absence of gender differences identification, interactional distortions of environmental and family responses are often present. Taking into account these emotional and behavioral problems researchers and clinicians are focusing on the design of innovative therapeutic approaches aimed to improve social capabilities of subjects with ASD. Thanks to the technological and scientific progresses of the last years, nowadays it is possible to create human-like robots with social and emotional capabilities. Furthermore it is also possible to analyze physiological signals inferring subjects' psycho-physiological state which can be compared with a behavioral analysis in order to obtain a deeper understanding of subjects reactions to treatments. In this work a preliminary evaluation of an innovative social robot-based treatment for subjects with ASD is described. The treatment consists in a complex stimulation and acquisition platform composed of a social robot, a multi-parametric acquisition system and a therapeutic protocol. During the preliminary tests of the treatment the subject's physiological signals and behavioral parameters have been recorded and used together with the therapists' annotations to infer the subjects' induced reactions. Physiological signals were analyzed and statistically evaluated demonstrating the possibility to correctly discern the two groups (ASD and normally developing subjects) with a classification percentage higher than 92%. Statistical analysis also highlighted the treatment capability to induce different affective states in subjects with ASDs more than in control subjects, demonstrating that the treatment is well designed and tuned on ASDs deficits and behavioral lacks.
- Aug 2012
- Autonomous Sensor Networks
In the last two decades, many research groups and industrial companies have been and are putting much efforts in developing and using fabrics in which electronics, digital components as well as computing can be embedded. These fabrics are identified as E-textiles (e.g., electronic textiles or smart textiles). Starting from the established concept, which asserts that future systems need to be more suitably interfaced with the humans with minimal discomfort and maximum acceptability, the possibility enabled by the E-textile platforms of developing wearable and intelligent technology in terms of everyday textiles and clothes, has made them one of the most important and interesting front-end between the biological and the technological world. One field of application of these innovative textiles is the ambient intelligence, where the use of wireless system network (WSN), body area network (BAN), or wireless body/personal area network (WB/PAN) has made it possible to integrate information coming from the environment, context awareness, and the habits of people during their activities, opening new areas of research on mental and emotional status as well as human behavior in different cultural environments. This chapter is focused on the research literature of the textile-based systems and aims at showing how and where they are currently used. Starting from the textile apparel, i.e. the technology used today for their construction, the chapter reports on the characterization, integration of electronic components and, finally, briefly it illustrates some E-textile-based WBAN platforms applications on network architecture for health care and lifestyle.
Bipolar disorders are characterized by an unpredictable behavior, resulting in depressive, hypomanic or manic episodes alternating with euthymic states. A multi-parametric approach can be followed to estimate mood states by integrating information coming from different physiological signals and from the analysis of voice. In this work we propose an algorithm to estimate speech features from running speech with the aim of characterizing the mood state in bipolar patients. This algorithm is based on an automatic segmentation of speech signals to detect voiced segments, and on a spectral matching approach to estimate pitch and pitch changes. In particular average pitch, jitter and pitch standard deviation within each voiced segment, are estimated. The performances of the algorithm are evaluated on a speech database, which includes an electroglottographic signal. A preliminary analysis on subjects affected by bipolar disorders is performed and results are discussed.
People affected by bipolar disorders experience alternating states of depression with episodes of mania or hypomania. This mental can lead to a poor handling of daily routines, can worsen personal relationships, and often can be life-threatening. This preliminary study aims at investigating how the autonomic nervous system, in terms of electrodermal activity, responds to specific controlled emotional stimuli in bipolar patients. More specifically, we present here a method to deploy the analysis of ElectroDermal Activity (EDA) to discriminate clinical mood states. EDA was analyzed by using a deconvolution method to separate tonic from phasic components. The three subjects recruited and the experimental protocol used here is part of the European project PSYCHE. Preliminary results show that the bipolar mood states can be related to electrodermal tonic activity.
- Jun 2012
This paper reports on performance evaluation of a preliminary system prototype based on a fabric glove, with integrated textile electrodes placed at the fingertips, able to acquire and process the electrodermal response (EDR) to discriminate affective states. First, textile electrodes have been characterized in terms of voltage-current characteristics and trans-surface electric impedance. Next, signal quality of EDR acquired simultaneously from textile and standard electrodes was comparatively evaluated. Finally, a dedicated experiment in which 35 subjects were enrolled, aiming at discriminating different affective states using only EDR was designed and realized. A new set of features extracted from non-linear methods were used, improving remarkably successful recognition rates. Results are, indeed, very satisfactory and promising in the field of affective computing.
Making use of the poroelastic theory for hydrated polymeric matrices, the ultrasound (US) propagation in a gel medium filled by spherical cells is studied . The model describes the connection between the poroelastic structure of living means and the propagation behavior of the acoustic waves. The equation of fast compressional wave, its phase velocity and its attenuation as a function of the elasticity, porosity and concentration of the cells into the gel external matrix are investigated. The outcomes of the theory agree with the measurements done on PVA gel scaffolds inseminated by porcine liver cells at various concentrations. The model is promising in the quantitative non-invasive estimation of parameters that could asses the change in the tissue structure, composition and architecture.
Affective processes appraise the salience of external stimuli preparing the agent for action. So far, the relationship between stimuli, affect, and action has been mainly studied in highly controlled laboratory conditions. In order to find the generalization of this relationship to social interaction, we assess the influence of the salience of social stimuli on human interaction. We constructed reality ball game in a mixed reality space where pairs of people collaborated in order to compete with an opposing team. We coupled the players with team members with varying social salience by using both physical and virtual representations of remote players (i.e., avatars). We observe that, irrespective of the team composition, winners and losers display significantly different inter- and intrateam spatial behaviors. We show that subjects regulate their interpersonal distance to both virtual and physical team members in similar ways, but in proportion to the vividness of the stimulus. As an independent validation of this social salience effect, we show that this behavioral effect is also displayed in physiological correlates of arousal. In addition, we found a strong correlation between performance, physiology, and the subjective reports of the subjects. Our results show that proxemics is consistent with affective responses, confirming the existence of a social salience effect. This provides further support for the so-called law of apparent reality, and it generalizes it to the social realm, where it can be used to design more efficient social artifacts. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
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.
- Apr 2012
This paper reports on a new methodology for the automatic assessment of emotional responses. More specifically, emotions are elicited in agreement with a bi-dimensional spatial localization of affective states. i.e. 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 both a neutral reference level. Standard methods as well as non-linear dynamic techniques were used to extract sets of features from the collected signals. The goal of this paper is to implement an automatic multi-class arousal/valence classifier comparing performance when extracted features from non-linear methods are used as alternative to standard features. Results show that, when non-linearly extracted features are used, the percentages of successful recognition dramatically increase. A good recognition accuracy (>90%) after 40-fold cross-validation steps for both arousal and valence classes was achieved by using the Quadratic Discriminant Classifier (QDC).
In this work we characterized the non-linear complexity of Heart Rate Variability (HRV) in short time series. The complexity of HRV signal was evaluated during emotional visual elicitation by using Dominant Lyapunov Exponents (DLEs) and Approximate Entropy (ApEn). We adopted a simplified model of emotion derived from the Circumplex Model of Affects (CMAs), in which emotional mechanisms are conceptualized in two dimensions by the terms of valence and arousal. Following CMA model, a set of standardized visual stimuli in terms of arousal and valence gathered from the International Affective Picture System (IAPS) was administered to a group of 35 healthy volunteers. Experimental session consisted of eight sessions alternating neutral images with high arousal content images. Several works can be found in the literature showing a chaotic dynamics of HRV during rest or relax conditions. The outcomes of this work showed a clear switching mechanism between regular and chaotic dynamics when switching from neutral to arousal elicitation. Accordingly, the mean ApEn decreased with statistical significance during arousal elicitation and the DLE became negative. Results showed a clear distinction between the neutral and the arousal elicitation and could be profitably exploited to improve the accuracy of emotion recognition systems based on HRV time series analysis.
In the last few years, much effort has been devoted to the development of wearable sensing systems able to monitor physiological, behavioral, and environmental parameters. Less has been done on the accurate testing and assessment of this instrumentation, especially when considering devices thought to be used in harsh environments by subjects or operators performing intense physical activities. This paper presents methodology and results of the evaluation of wearable physiological sensors under these conditions. The methodology has been applied to a specific textile-based prototype, aimed at the real-time monitoring of rescuers in emergency contexts, which has been developed within a European funded project called ProeTEX. Wearable sensor measurements have been compared with the ones of suitable gold standards through Bland-Altman statistical analysis; tests were realized in controlled environments simulating typical intervention conditions, with temperatures ranging from 20 °C to 45 °C and subjects performing mild to very intense activities. This evaluation methodology demonstrated to be effective for the definition of the limits of use of wearable sensors. Furthermore, the ProeTEX prototype demonstrated to be reliable, since it produced negligible errors when used for up to 1 h in normal environmental temperature (20 °C and 35 °C) and up to 30 min in harsher environment (45 °C).
An ultra wideband (UWB) system-on-chip radar sensor for respiratory rate monitoring has been realized in 90 nm CMOS technology and characterized experimentally. The radar testchip has been applied to the contactless detection of the respiration activity of adult and baby. The field operational tests demonstrate that the UWB radar sensor detects the respiratory rate of person under test (adult and baby) associated with sub-centimeter chest movements, allowing the continuous-time non-invasive monitoring of hospital patients and other people at risk of obstructive apneas such as babies in cot beds, or other respiratory diseases.
- Aug 2011
The goal of this work is to investigate EEG (ElectroEncephaloGram) dynamics after drug intake in patients being in states of Disorders Of Consciousness (DOC) after brain injury. Four patients were involved in the study. All the patients exhibit cerebral lesions located in the same anatomical region. Two nonlinear indexes, such as Lempel-Ziv Complexity (LZC) and Approximate Entropy (ApEn), along with power spectra, were calculated for EEG signals gathered from electrodes placed on both injured and non-injured regions. Experimental results show that after drug administration the two nonlinear indexes calculated from EEG taken from injured regions increase (p < 0.001) while power spectra decrease or remain unchanged. These results do not pretend to draw conclusions about consciousness level either suggest promising therapeutical treatments, but represent only an experimental evidence about the change in the EEG complexity after drug administration.
- Jun 2011
This paper investigates the use of Higher Order Spectra parameters to identify the most common multiple cardiac arrhythmias. In detail, we calculated magnitude of bispectrum, three values of bispectrum entropy, mean and variance of the phase of bispectrum integrated over a particular region wherein no bispectrum aliasing is assumed. This set of features is used to distinguish normal QRS from five different classes of arrhythmia over a large amount of normal and pathologic ECG signals. An accurate parametric and non-parametric analysis of these feature distributions is also performed in order to identify the optimum classifier. Experimental tests were performed on signals gathered from the MIT-BIH Arrhythmias Database, and mean and standard deviation of all confusion matrixes obtained from 20 steps of cross validation are shown. Results showed that the bispectrum is high performance, reliable and robust method to identify arrhythmias.
- Mar 2011
Eye gaze trackers (EGTs) are generally developed for scientific exploration in controlled environments or laboratories and data have been used in ophthalmology, neurology, psychology, and related areas to study oculomotor characteristics and abnormalities, and their relation to cognition and mental states. The illumination is one of the most restrictive limitation of the EGTs, due to a problem of pupil center estimation during illumination changes. Most of the current systems, indeed, work under controlled illumination conditions either in dark or indoor environments, e.g. using infrared sources or conforming the sources of light to fixed levels or pointing directions. This work is focused on exploring and comparing several photometric normalization techniques to improve EGT systems during light changes. In particular, a new wearable and wireless eye tracking system (HATCAM) is used for testing the different techniques in terms of real-time capability, eye tracking and pupil area detection. Embedding real-time image enhancement into the HATCAM can make it an innovative and robust system for eye tracking in different lighting conditions, i.e. darkness, sunlight, indoor and outdoor environments.
Eye Gaze Trackers (EGTs) and pupil size variation are generally developed for scientific investigation in controlled environments or laboratories and data are used in several fields of application such as ophthalmology, neurology, or psychology, with the aim of studying oculomotor characteristics and abnormalities. Very often, the focus of these studies is the identification of cognitive and mental states. This preliminary work aims at investigating if eye tracking and pupil size variation can provide useful cues to discriminate emotional states induced by viewing images at different arousal content. Here we propose a new wearable and wireless EGT, hereinafter called HATCAM, able to robustly enable eye tracking and pupil area detection. Although very preliminary, results are very promising for affective computing applications.
Employing the poroelastic theory of acoustic waves in gels, the ultrasound (US) propagation in a gel medium filled by poroelastic spherical cells is studied. The equation of fast compressional wave, the phase velocity and the attenuation as a function of the elasticity, porosity and concentration of the cells into the gel matrix are investigated. The outcomes of the theory agree with the preliminary measurements done on PVA gel scaffolds inseminated by porcine liver cells at various concentrations. The feasibility of a non-invasive tech-nique for the health assessment of soft biological tissues steaming by the model is analyzed.
- Jan 2011
- Wearable Monitoring Systems
When designing wearable systems to be used for physiological and biomechanical parameters monitoring, it is important to integrate sensors easy to use, comfortable to wear, and minimally obtrusive. Wearable systems include sensors for detecting physiological signs placed on-body without discomfort, and possibly with capability of real-time and continuous recording. The system should also be equipped with wireless communication to transmit signals, although sometimes it is opportune to extract locally relevant variables, which are transmitted when needed.
- Jan 2011
In this chapter we present two innovative contributions derived by the System-on-Chip (SoC) approach combining standard microelectronic technologies with the life and geo-sciences. The former regards a SoC radar sensor for contactless cardiopulmonary monitoring. The latter focuses on a SoC radiometer for temperature remote sensing. For both micro-sensors we report the framework and motivation, theoretical concepts, current results, and the future direction of ongoing research by the authors. © 2010 by SciTech Publishing, Raleigh, NC. All rights reserved.
In this paper we implement an automatic procedure that is to be embedded in a wearable system in order to discriminate five arrhythmic classes of QRS complexes from normal ones. Due to the limited hardware resources offered by the wearable system, several requirements such as low computational cost, memory usage, reliability and real-time have to be addressed. In order to better comply with these requirements, the classification process is performed using features that can easily be extracted from the signals, i.e. magnitude and phase of the Fourier Transform (FT) applied to the QRS complexes. The ECG signals, from which QRS complexes are extracted, are gathered from the MIT-Arrhythmias Database. More specifically, three datasets of features are created: the first (alpha) is obtained from the magnitude, the second (beta) from the phase, and the third (gamma) from the union of the two. According to the results of the Royston Multivariate Normality Test, which verifies the gaussianity of the distribution of the three sets of features, a parametric, Nearest Mean Classifier (NMC), or non-parametric, MultiLayer Perceptron (MLP) classifier is used. The comparative performance evaluation is showed in terms of a confusion matrix obtained from twenty steps of cross validation. The matrices report the percentage of successful recognition of the six classes.
This paper aims at testing the ability of textile electrodes to effectively acquire electrodermal responses (EDRs). EDRs are acquired from sixteen healthy subjects in order to comparatively evaluate the performance of textile versus standard silver/silver chloride (Ag/AgCl) electrodes. The acquired signals are analyzed in the time and frequency domain, and a statistical approach is used to validate the system. Moreover, a characterization of a textile electrode, in terms of electrode impedance measurement and a current/voltage diagram, is carried out in the frequency range of EDR usability (from 0.01 Hz to 2 Hz). The results show good performance.
Several advances have been done in wireless body area networks for bio-monitoring, as confirmed by the fine tuning of ad-hoc standards for networking, controlling and managing a distributed sensor platform around the human body. Efficient wireless networks are becoming commercially available for several applications. In spite of that, sets of “invisible” sensors for monitoring bio-signals are still expected as enabling technology for mass-market applications. This paper aims at reviewing the current state of the art, and identifying some existing open issues and added functionalities desirable for the future in heart health wireless assistance. Some results emerging from the on-going research are presented, focusing on the implementation of an innovative contact-less miniaturized radar sensor for cardiopulmonary monitoring within a wearable textile platform. The expected future developments toward a wireless body area network are reported and discussed.
- Aug 2010
This paper investigates the possibility of using Electrodermal Response, acquired by a sensing fabric glove with embedded textile electrodes, as reliable means for emotion recognition. Here, all the essential steps for an automatic recognition system are described, from the recording of physiological data set to a feature-based multiclass classification. Data were collected from 35 healthy volunteers during arousal elicitation by means of International Affective Picture System (IAPS) pictures. Experimental results show high discrimination after twenty steps of cross validation.
- Jun 2010
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.
Awards & Achievements (1)
Award · Jun 2014
Best Paper - International Academy, Research, and Industry Association