Search

Objective: To characterize cognitive workload (CWL) of cardiac surgery team members in a real-world setting during coronary artery bypass grafting (CABG) surgery using providers' heart rate variability (HRV) data as a surrogate measure of CWL., Methods: HRV was collected from the surgeon, anesthesiologist, perfusionist, and scrub nurse, and audio/video recordings were made during isolated, nonemergency CABG surgeries (n = 27). Eight surgical phases were annotated by trained researchers, and HRV was calculated for each phase., Results: Significant differences in CWL were observed within a given role across surgical phases. Results are reported as predicted probability (95% confidence interval [CI]). CWL was significantly higher for anesthesiologists during "preparation and induction" (0.57; 95% CI, 0.42-0.71) and "anastomoses" (0.44; 95% CI, 0.30-0.58) compared to other phases, and the same held for nurses during the "opening" (0.51; 95% CI, 0.37-0.65) and "postoperative" (0.68; 95% CI, 0.42-0.86) phases. Additional significant differences were observed between roles within a given surgical phase. For example, surgeons had significantly higher CWL during "anastomoses" (0.81; 95% CI, 0.69-0.89) compared to all other phases, and the same was true of perfusionists during the "opening" (0.79; 95% CI, 0.66-0.88) and "prebypass preparation" (0.50; 95% CI, 0.36-0.64) phases., Conclusions: Our innovative analysis demonstrates that CWL fluctuates across surgical procedures by role and phase, which may reflect the distribution of primary tasks. This corroborates earlier findings from self-report measures. The data suggest that team-wide, peak CWL during a phase decreases from early phases of surgery through initiation of cardiopumonary bypass (CPB), rises during anastomosis, and decreases after termination of CPB. Knowledge of these trends could encourage the adoption of behaviors to enhance team dynamics and performance., Competing Interests: Conflict of Interest Statement The authors reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest., (Copyright © 2024 The American Association for Thoracic Surgery. All rights reserved.)

Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes., Methods: This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one's demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state., Results: Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models' ability to accurately estimate healthcare outcomes for the sensitive groups., Discussion: These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Yang, El-Attar and Chaspari.)

Like all job applicants, veterans have to face the ubiquitous employment interview and pass this potential hurdle to civilian sector employment. So, because of the uniqueness of transitioning from the military to civilian employment, the present paper sought to identify perceived interviewing strengths and weaknesses of veteran interviewees from (a) the perspective of civilian sector human resource professionals (i.e. hiring personnel) with experience interviewing veterans (Study 1, five focus groups, N  = 14), and (b) veterans (Study 2, N  = 93). Qualitative analysis of the focus group transcripts resulted in the emergence of two theme categories: (1) veteran interviewee strengths and (2) veteran interviewee weaknesses. This information guided the development of a 10-item survey that was completed by 93 veterans (Study 2). In its totality, the results (from both Study 1 and Study 2) indicated that communication of soft skills, confidence, and professionalism were perceived to be strengths that veterans displayed during civilian employment interviews, and conversely, the ineffective translation and communication of relevant technical skills acquired in the military, use of military jargon, and nervousness were considered to be weaknesses. Recommendations to capitalize on the strengths and mitigate the weaknesses are presented.

Understanding how communication processes contribute to well-functioning versus distressed couple relationships has relied largely on brief, laboratory-based conversations. Harnessing technological advancements, the present study extends the literature by capturing couples' naturalistic communication over one full day at Time 1 (T1). This study tested associations between data-driven categories of couple communication behaviors and relationship outcomes (i.e., relationship aggression, satisfaction, and dissolution) at Time 2 (T2), approximately 1 year later. Emerging adults in different-gender dating couples ( n = 106 couples; 212 individuals; M age = 22.57 ± 2.44; M relationship length = 30.49 months ± 24.05; 72.2% non-White) were each provided a smartphone programmed to audio record approximately 50% of a typical day. Interactions between partners were transcribed and coded for location, activity, affect, and a range of positive and negative communication behaviors for each partner. Even after controlling for T1 assessments of the relevant outcome, one's own hostility and one's partner's hostility at T1 were each positively associated with T2 relationship aggression and negatively associated with T2 relationship satisfaction. One's own withdrawal at T1 was positively associated with T2 relationship aggression perpetration, whereas one's partner's withdrawal was negatively linked to relationship satisfaction at T2. One's own playfulness, unexpectedly, was linked to lower subsequent relationship satisfaction. Withdrawal increased the likelihood of relationship dissolution, whereas warmth and playfulness decreased the likelihood of dissolution. The relevance of couples' ordinary, everyday communication for meaningful relationship outcomes is discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Physiological linkage refers to moment-to-moment, time-linked coordination in physiological responses among people in close relationships. Although people in romantic relationships have been shown to evidence linkage in their physiological responses over time, it is still unclear how patterns of covariation relate to in-the-moment, as well as general levels of, relationship functioning. In the present study with data collected between 2014 and 2017, we capture linkage in electrodermal activity (EDA) in a diverse sample of young-adult couples, generally representative and generalizable to the Los Angeles community from which we sampled. We test how naturally occurring, shifting feelings of closeness with and annoyance toward one's partner relate to concurrent changes in levels of physiological linkage over the course of 1 day. Additionally, we examine how linkage relates to overall relationship satisfaction. Results showed that couples evidenced significant covariation in their levels of physiological arousal in daily life. Further, physiological linkage increased during hours that participants felt close to their romantic partners but not during hours that participants felt annoyed with their partners. Finally, those participants with overall higher levels of relationship satisfaction showed lower levels of linkage over the day of data collection. These findings highlight how individuals respond in sync with their romantic partners and how this process ebbs and flows in conjunction with the shifting emotional tone of their relationships. The discussion focuses on how linkage might enhance closeness or, alternatively, contribute to conflict escalation and the potential of linkage processes to promote positive interpersonal relationships. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.

Introduction: Intelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct., Methods: The metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group., Results: The outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect., Discussion: This study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Paromita, Mundnich, Nadarajan, Booth, Narayanan and Chaspari.)

Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications., (© 2023. The Author(s).)

Does talking about loss with a romantic partner have salutary personal and relationship effects? Prior evidence reveals the benefits of emotional disclosure in couple relationships, yet disclosure about loss has been overlooked in research on couple communication. Using a novel communication paradigm with young-adult heterosexual romantic partners ( N = 114 couples), we investigated emotions, physiological arousal (skin conductance responses [SCR]), and relationship closeness when narrating a personal loss and listening to the partner's loss, and compared these loss discussions to discussions about desired relationship changes. Based on partners' self-reports, narrating loss elicited more vulnerable and, unexpectedly, more antagonistic emotions. Both narrating and listening to loss produced higher self-reported partner closeness, compared to discussing change. In support of the physiological benefits of disclosure, women's SCRs decreased over the discussion when they narrated their own loss. However, both women and men as listeners show a general trend of increasing SCRs over the discussion, suggesting the challenges of being a responsive partner. Moreover, in line with the putative protective effects of partners' biological interdependencies, partner closeness also was higher when both partners showed synchronous decreasing SCR as women narrated their loss. Although limited to young couples in relatively short relationships, these findings reveal some potential benefits of talking about loss in the context of romantic relationships. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Sensor-based assessment in combination with machine learning algorithms provide the potential to augment current practices of the (early) diagnosis of cognitive impairment. The goal of this paper is to detect cognitive impairment in elderly adults using sensor-based measures installed in the home. Longitudinal time-series data of sensor signals are analyzed with Poisson process (PP) models and supervised machine learning algorithms to identify individuals with mild cognitive impairment (MCI) and dementia. We examine two types of PP models: a homogeneous PP which assumes a constant rate of change for each sensor, and a non-homogeneous PP which incorporates contextual information by separately estimating the arrival rate for each task. Our results indicate that the proposed approach can effectively distinguish between patients with dementia and healthy individuals, as well as patients with MCI and healthy individuals based on the sensor-based PP features. Sensor-based assessment that relies on the non-homogeneous PP is further found to be more effective for the task of interest compared to homogeneous PP, as well as expert-based assessment. Findings from this research have the potential to help detect the early onset of cognitive impairment in elderly adults, and demonstrate the ability of computational models and machine learning to predict cognitive health, thus, contributing toward advancing aging-in-place. Clinical Relevance-This examines a computational method to quantify cognitive decline for elderly adults using home-based sensors. eventually contributing to ambulatory clinical biomarkers for dementia.

Diet monitoring is an essential intervention component for a number of diseases, from type 2 diabetes to cardiovascular diseases. However, current methods for diet monitoring are burdensome and often inaccurate. In prior work, we showed that continuous glucose monitors (CGMs) may be used to predict meal macronutrients (e.g., carbohydrates, protein, fat) by analyzing the shape of the post-prandial glucose response. In this study, we examine a number of additional dietary biomarkers in blood by their ability to improve macronutrient prediction, compared to using CGMs alone. For this purpose, we conducted a nutritional study where (n = 10) participants consumed nine different mixed meals with varied but known macronutrient amounts, and we analyzed the concentration of 33 dietary biomarkers (including amino acids, insulin, triglycerides, and glucose) at various times post-prandially. Then, we built machine learning models to predict macronutrient amounts from (1) individual biomarkers and (2) their combinations. We find that the additional blood biomarkers provide complementary information, and more importantly, achieve lower normalized root mean squared error (NRMSE) for the three macronutrients (carbohydrates: 22.9%; protein: 23.4%; fat: 32.3%) than CGMs alone (carbohydrates: 28.9%, t(18) =1.64, p =0.060; protein: 46.4%, t(18) =5.38, p 0.001; fat: 40.0%, t(18) =2.09, p =0.025). Our main conclusion is that augmenting CGMs to measure these additional dietary biomarkers improves macronutrient prediction performance, and may ultimately lead to the development of automated methods to monitor nutritional intake. This work is significant to biomedical research as it provides a potential solution to the long-standing problem of diet monitoring, facilitating new interventions for a number of diseases.

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.

The present study investigated whether the presence of a romantic partner in daily life is associated with attenuated sympathetic nervous system responses. Additionally, romantic attachment style was tested as a moderator. For one day, 106 heterosexual young adult dating couples wore ambulatory sensors that monitored electrodermal activity (EDA) - an index of sympathetic arousal. Couples reported whether they were together or apart for every hour of the data collection day. Men and women exhibited lower EDA during hours in which their partner was present compared to hours in which they were absent. Additionally, romantic attachment style moderated this association; those who had low anxious attachment showed a stronger attenuating effect of partner presence compared to those with higher anxious attachment. Similarly, those who had low avoidant attachment showed heightened effects of partner presence compared to those with higher avoidant attachment. Romantic partner presence may facilitate everyday health-promoting physiological processes., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics. Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability. Methods: A multidisciplinary overview and literature synthesis. Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability. Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SZ declared a shared affiliation, though no other collaboration, with the authors to the handling Editor., (Copyright © 2021 Zanwar, Kim, Kim, Manser, Ham, Chaspari and Ahn.)

Background: Although past longitudinal research demonstrates that romantic partners affect one another's health outcomes, considerably less is known about how romantic experiences "get under the skin" in everyday life., Purpose: The current study investigated whether young couples' naturally occurring feelings of closeness to and annoyance with each other during waking hours were associated with their overnight cardiovascular activity., Methods: Participants were 63 heterosexual young adult dating couples (Mage = 23.07). Using ecological momentary assessments, couples reported their hourly feelings of closeness to and annoyance with their partners across 1 day; subsequent overnight heart rate was captured through wearable electrocardiogram biosensors. Actor-partner interdependence models tested whether individuals' overnight heart rate varied as a function of (a) their own daytime feelings of closeness and annoyance (actor effects) and (b) their partner's daytime feelings of closeness and annoyance (partner effects) while controlling for daytime heart rate., Results: Although young adults' feelings of romantic closeness and annoyance were unrelated to their own overnight heart rate (i.e., no actor effects), gender-specific partner effects emerged. Young men's nocturnal heart rate was uniquely predicted by their female partner's daytime relationship feelings. When women felt closer to their partners during the day, men exhibited lower overnight heart rate. When women felt more annoyed with their partners during the day, men exhibited heightened overnight heart rate., Conclusions: The findings illustrate gender-specific links between couple functioning and physiological arousal in the everyday lives of young dating couples, implicating physiological sensitivity to partner experiences as one potential pathway through which relationships affect health., (© Society of Behavioral Medicine 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Prolonged influence of negative emotions can result in clinical depression or anxiety, and while many prescribed techniques exist, music therapy approaches, coupled with psychotherapy, have shown to help lower depressive symptoms, supplementing traditional treatment approaches. Identifying the appropriate choice of music, therefore, is of utmost importance. Selecting appropriate playlists, however, are challenged by user feedback that may inadvertently select songs that amplify the negative effects. Therefore, this work uses electroencephalogram (EEG) that automatically identifies the emotional impact of music and trains a reinforcement-learning approach to identify an adaptive personalized playlist of music to lead to improved emotional states. This work uses data from 32 users, collected in the publicly available DEAP dataset, to select songs for users that guide them towards joyful emotional states. Using a domain-specific reward-shaping function, a Q-learning agent is able to correctly guide a majority of users to the target emotional states, represented in a common emotion wheel. The average angular error of all users is 57°, with a standard deviation of 2.8 and the target emotional state is achieved.Clinical relevance- Music therapy for improving clinical depression and anxiety can be supplemented by additional emotion-guided music decisions in remote and personal settings by using automated techniques to capture emotional state and identify music that best guides users to target joyful states.

As hospital workers face a growing number of patients and have to meet increasingly rigorous standards of care, their ability to successfully modulate their emotional reactions and flexibly handle stress presents a significant challenge. This paper examines a multimodal signal-driven way to quantify emotion self-regulation and stress spillover through a dynamical systems model (DSM). The proposed DSM models day-to-day changes of emotional arousal, captured through speech, physiology, and daily activity measures, and its interplay with daily stress. The parameters of the DSM quantify the degree of self-regulation and stress spillover, and are associated with work performance and cognitive ability in a multimodal dataset of 130 full-time hospital workers recorded over a 10-week period. Linear regression experiments indicate the effectiveness of the proposed features to reliably estimate individuals' work performance and cognitive ability, providing significantly higher Pearson's correlations compared to aggregate measures of emotional arousal. Results from this study demonstrate the importance of quantifying oscillatory behaviors from longitudinal ambulatory signals and can potentially deepen our understanding of emotion self-regulation and stress spillover using signal-driven measurements, which complement self-reports and provide estimates of the psychological constructs of interest in a fine-grained time resolution.

Individuals exposed to aggression and who perpetrate aggression against others show differences in their physiological activation during stress; the goal of the present study is to investigate physiological stress reactivity as a factor contributing to the intergenerational transmission of aggression. To test associations between family-of-origin aggression (FOA), physiological reactivity in daily life, and dating aggression perpetration, we used ecological momentary assessment to monitor fluctuations in young adult (M age  = 23.1 years) dating couples' electrodermal activity (EDA) over an entire day and examined how naturally-occurring bouts of annoyance between partners relate to EDA, FOA, and dating aggression perpetration. Dating perpetration was linked to lower general levels of EDA in both men and women, while FOA was linked to lower general levels of EDA in men only. For women, multi-group, multilevel models showed that FOA and dating aggression perpetration moderated the association between feeling annoyed and EDA, such that those with greater FOA and dating aggression perpetration showed greater EDA reactivity during naturally-occurring relationship stress. Furthermore, this pattern of EDA reactivity mediated the link between FOA and dating aggression perpetration in women. These results provide evidence that FOA and dating aggression perpetration are linked to patterns of physiological responsivity in everyday life and suggest that these patterns could be important factors contributing to the intergenerational transmission of aggression., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Continuous glucose monitoring (CGM) of patients with diabetes allows the effective management of the disease and reduces the risk of hypoglycemic or hyperglycemic episodes. Towards this goal, the development of reliable CGM models is essential for representing the corresponding signals and interpreting them with respect to factors and outcomes of interest. We propose a sparse decomposition model to approximate CGM time-series as a linear combination of a small set of exemplar atoms, appropriately designed through parametric functions to capture the main fluctuations of the CGM signal. Sparse decomposition is performed through the orthogonal matching pursuit (OMP). Results indicate that the proposed model provides 0.1 relative reconstruction error with 0.8 compression rate on a publicly available dataset containing 25 patients diagnosed with Type 1 diabetes. The atoms selected from the OMP procedure can be further interpreted in relation to the clinically meaningful components of the CGM signal (e.g. glucose spikes, hypoglycemic episodes, etc.

Wearable technology permeates every aspect of our daily life increasing the need of reliable and interpretable models for processing the large amount of biomedical data. We propose the EDA-Gram, a multidimensional fingerprint of the electrodermal activity (EDA) signal, inspired by the widely-used notion of spectrogram. The EDA-Gram is based on the sparse decomposition of EDA from a knowledge-driven set of dictionary atoms. The time axis reflects the analysis frames, the spectral dimension depicts the width of selected dictionary atoms, while intensity values are computed from the atom coefficients. In this way, EDA-Gram incorporates the amplitude and shape of Skin Conductance Responses (SCR), which comprise an essential part of the signal. EDA-Gram is further used as a foundation for signal-specific feature design. Our results indicate that the proposed representation can accentuate fine-grain signal fluctuations, which might not always be apparent through simple visual inspection. Statistical analysis and classification/regression experiments further suggest that the derived features can differentiate between multiple arousal levels and stress-eliciting environments for two datasets.

Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis-Hastings-within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from appropriate candidate-generating densities. We further show that the corresponding Markov Chain is uniformly ergodic ensuring its convergence to a stationary distribution independently of the initial state. Results on synthetic data and real biomedical signals indicate that our approach offers advantages in terms of signal reconstruction compared to previously proposed Steepest Descent and Equiangular Tight Frame methods. This paper demonstrates the ability of Bayesian learning to generate parametric dictionaries that can reliably represent the exemplar data and provides the foundation towards inferring the entire variable set of the sparse approximation problem for signal denoising, adaptation and other applications.

Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven method to represent electrodermal activity (EDA), a psychophysiological signal linked to stress, affect, and cognitive processing. We build EDA-specific dictionaries that accurately model both the slow varying tonic part and the signal fluctuations, called skin conductance responses (SCR), and use greedy sparse representation techniques to decompose the signal into a small number of atoms from the dictionary. Quantitative evaluation of our method considers signal reconstruction, compression rate, and information retrieval measures, that capture the ability of the model to incorporate the main signal characteristics, such as SCR occurrences. Compared to previous studies fitting a predetermined structure to the signal, results indicate that our approach provides benefits across all aforementioned criteria. This paper demonstrates the ability of appropriate dictionaries along with sparse decomposition methods to reliably represent EDA signals and provides a foundation for automatic measurement of SCR characteristics and the extraction of meaningful EDA features.

Objectives: The development of ecologically valid procedures for collecting reliable and unbiased emotional data towards computer interfaces with social and affective intelligence targeting patients with mental disorders., Methods: Following its development, presented with, the Athens Emotional States Inventory (AESI) proposes the design, recording and validation of an audiovisual database for five emotional states: anger, fear, joy, sadness and neutral. The items of the AESI consist of sentences each having content indicative of the corresponding emotion. Emotional content was assessed through a survey of 40 young participants with a questionnaire following the Latin square design. The emotional sentences that were correctly identified by 85% of the participants were recorded in a soundproof room with microphones and cameras. A preliminary validation of AESI is performed through automatic emotion recognition experiments from speech., Results: The resulting database contains 696 recorded utterances in Greek language by 20 native speakers and has a total duration of approximately 28 min. Speech classification results yield accuracy up to 75.15% for automatically recognizing the emotions in AESI., Conclusions: These results indicate the usefulness of our approach for collecting emotional data with reliable content, balanced across classes and with reduced environmental variability.