A Framework for Psychophysiological Classification within a Cultural Heritage Context Using Interest

This article presents a psychophysiological construct of interest as a knowledge emotion and illustrates the importance of interest detection in a cultural heritage context. The objective of this work is to measure and classify psychophysiological reactivity in response to cultural heritage material presented as visual and audio. We present a data processing and classification framework for the classification of interest. Two studies are reported, adopting a subject-dependent approach to classify psychophysiological signals using mobile physiological sensors and the support vector machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material using psychophysiological feature data and a machine learning approach, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience designed to adapt the provision of information to sustain the interest of the visitor

Wearable Technology for Mental Wellness Monitoring and Feedback

This thesis investigates the transformative potential of wearable monitoring devices in empowering individuals to make positive lifestyle changes and enhance mental well-being. The primary objective is to assess the efficacy of these devices in addressing mental health issues, with a specific focus on stress and anxiety biomarkers. The research includes a systematic literature review that uniquely emphasizes integrating wearable technology into mental wellness, spanning diverse domains such as electronics, wearable technology, machine learning, and data analysis. This novel systematic literature review encompasses the period from 2010 to 2023, examining the profound impact of the Internet of Things (IoT) across various sectors, particularly healthcare. The thesis extensively explores wearable technologies capable of identifying a broad spectrum of human biomarkers and stress-related indicators, emphasizing their potential benefits for healthcare professionals. Challenges faced by participants and researchers in the practical implementation of wearable technology are addressed through survey analysis, providing substantial evidence for the potential of wearables in bolstering mental health within professional environments. Meticulous data analysis gathering from biosignals captured by wearables investigates the impact of stress factors and anxiety on individuals' mental well-being. The study concludes with a thorough discussion of the findings and their implications. Additionally, integrating Photoplethysmography (PPG) devices is highlighted as a significant advancement in capturing vital biomarkers associated with stress and mental well-being. Through light-based technology, PPG devices monitor blood volume changes in microvascular tissue, providing real-time information on heart rate variability (HRV). This non-invasive approach enables continuous monitoring, offering a dynamic understanding of physiological responses to stressors. The reliability of wearable devices equipped with PPG and Electroencephalography (EEG) sensors is emphasized in capturing differences in subject biomarkers. EEG devices measure brainwave patterns, providing insights into neural activity associated with stress and emotional states. The combination of PPG and EEG data enhances the precision of stress and mental well-being assessments, offering a holistic approach that captures peripheral physiological responses and central nervous system activity. In conclusion, integrating PPG devices with subjective methods and EEG sensors significantly advances stress and mental well-being assessment. This multidimensional approach improves measurement accuracy, laying the foundation for personalized interventions and innovative solutions in mental health care. The thesis also evaluates body sensors and their correlation with medically established gold references, exploring the potential of wearable devices in advancing mental health and well-being

Caregiving is also thinking: Maternal cognitions in child abuse and neglect

Child-maltreatment has long been recognized as a serious and prevalent social problem with multiple and long-term consequences for child development. This work examines child-maltreatment based on a Social Information Processing model, emphasizing the role of cognitive representations, and errors and biases in processing caregiving-related information on parental responses. Six articles present (a) a set of meta-analyses about the relation between parents’ socio-cognitive variables and child-maltreatment, (b) a systematic review of implicit measures to assess parental cognitions in the context of maltreatment; (c) map and compare cognitive representations about parenting of referred and non-referred mothers; and (d) examine the association of implicit and explicit parental attitudes and (e) errors in emotion recognition, with self- and professionals-reported child abuse and neglect. The results of the reviews indicated that the associations of parental schemata and biased information processing with child maltreatment are significant, as well as that the potential of implicit measures in assessing parental cognitions may be valuable. Moreover, the empirical studies support the hypothesis that maladaptive parenting is characterized by rigidity schemata and associated with inadequate parental attitudes and errors in perceiving children’s emotional signals, but mostly for neglect and particularly when hetero-reported. Theoretically, these findings support the SIP model and emphasize the potential utility of socio-cognitive approaches in the evaluation and explanation of child maltreatment. The reported studies also represent a valuable methodological approach for assessing both maltreatment and parental cognitions. Overall, this work presents a contribution to the still emerging research about parental cognitions in the context of child maltreatment, with important implications for research and intervention.O mau-trato infantil é amplamente reconhecido como um problema social prevalente, com consequências múltiplas e a longo-prazo para o desenvolvimento da criança. O presente trabalho examina o mau-trato à luz do modelo de Processamento de Informação Social (SIP), acentuando o papel das representações cognitivas, e de erros e enviesamentos no processamento da informação relativa ao cuidar, nas respostas parentais. Seis artigos apresentam (a) um conjunto de meta-análises sobre a relação entre variáveis sociocognitivas dos pais e o mau-trato, (b) uma revisão sistemática de medidas implícitas utilizadas para avaliar essas cognições em contextos de mau-trato; (c) mapeiam e comparam representações sobre parentalidade de mães sinalizadas e não-sinalizadas; e (d) examinam a relação entre atitudes parentais implícitas e explícitas e (e) erros no reconhecimento de emoções das crianças, e o abuso e negligência, auto e hétero-reportados. Os resultados dos estudos de revisão indicam que as associações entre esquemas cognitivos parentais e enviesamentos no processamento da informação e o mau-trato são significativas, assim como o potencial das medidas implícitas na avaliação das cognições parentais. Os estudos empíricos sugerem especificamente que a parentalidade desadaptativa é caracterizada por esquemas cognitivos rígidos, atitudes parentais inadequadas e erros na perceção dos sinais emocionais da criança, sobretudo na negligência, e quando reportada pelos profissionais. Teoricamente, estes resultados suportam o modelo SIP e enfatizam o potencial das abordagens sociocognitivas na avaliação e explicação do mau-trato. Os estudos reportados representam também um importante contributo metodológico para a avaliação do mau-trato e das cognições parentais. Este trabalho apresenta assim uma contribuição para a emergente pesquisa sobre cognições parentais no contexto do mau-trato, com implicações importantes para a investigação e intervenção

Non-invasive wearable sensing systems for continuous health monitoring and long-term behavior modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 212-228).Deploying new healthcare technologies for proactive health and elder care will become a major priority over the next decade, as medical care systems worldwide become strained by the aging populations. This thesis presents LiveNet, a distributed mobile system based on low-cost commodity hardware that can be deployed for a variety of healthcare applications. LiveNet embodies a flexible infrastructure platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification capabilities. Using LiveNet, we are able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. Most clinical sensing technologies that exist have focused on accuracy and reliability, at the expense of cost-effectiveness, burden on the patient, and portability. Future proactive health technologies, on the other hand, must be affordable, unobtrusive, and non-invasive if the general population is going to adopt them.(cont.) In this thesis, we focus on the potential of using features derived from minimally invasive physiological and contextual sensors such as motion, speech, heart rate, skin conductance, and temperature/heat flux that can be used in combination with mobile technology to create powerful context-aware systems that are transparent to the user. In many cases, these non-invasive sensing technologies can completely replace more invasive diagnostic sensing for applications in long-term monitoring, behavior and physiology trending, and real-time proactive feedback and alert systems. Non-invasive sensing technologies are particularly important in ambulatory and continuous monitoring applications, where more cumbersome sensing equipment that is typically found in medical and clinical research settings is not usable. The research in this thesis demonstrates that it is possible to use simple non-invasive physiological and contextual sensing using the LiveNet system to accurately classify a variety of physiological conditions. We demonstrate that non-invasive sensing can be correlated to a variety of important physiological and behavioral phenomenon, and thus can serve as substitutes to more invasive and unwieldy forms of medical monitoring devices while still providing a high level of diagnostic power.(cont.) From this foundation, the LiveNet system is deployed in a number of studies to quantify physiological and contextual state. First, a number of classifiers for important health and general contextual cues such as activity state and stress level are developed from basic non-invasive physiological sensing. We then demonstrate that the LiveNet system can be used to develop systems that can classify clinically significant physiological and pathological conditions and that are robust in the presence of noise, motion artifacts, and other adverse conditions found in real-world situations. This is highlighted in a cold exposure and core body temperature study in collaboration with the U.S. Army Research Institute of Environmental Medicine. In this study, we show that it is possible to develop real-time implementations of these classifiers for proactive health monitors that can provide instantaneous feedback relevant in soldier monitoring applications. This thesis also demonstrates that the LiveNet platform can be used for long-term continuous monitoring applications to study physiological trends that vary slowly with time.(cont.) In a clinical study with the Psychiatry Department at the Massachusetts General Hospital, the LiveNet platform is used to continuously monitor clinically depressed patients during their stays on an in-patient ward for treatment. We show that we can accurately correlate physiology and behavior to depression state, as well as to track changes in depression state over time through the course of treatment. This study demonstrates how long-term physiology and behavioral changes can be captured to objectively measure medical treatment and medication efficacy. In another long-term monitoring study, the LiveNet platform is used to collect data on people's everyday behavior as they go through daily life. By collecting long-term behavioral data, we demonstrate the possibility of modeling and predicting high-level behavior using simple physiologic and contextual information derived solely from ambulatory mobile sensing technology.by Michael Sung.Ph.D

A manifestation of all life : intersections of virtue ethics, philosophy of emotion, and philosophy of literature.

This dissertation offers a brief survey of the rise of reason and the tum to the self at the expense of emotion in Western thought. This marginalization of emotion has had deleterious effects on two areas: the cultivation of virtue and the intersubjectivity necessary to sustain human flourishing. Using current research in neuropsychology the dissertation argues that reason and emotion form a dynamic partnership in the process of attaining reliable knowledge. Moreover, the dissertation argues that the emotional experience necessary for the cultivation of virtue, as well as the ability to attend to the emotional lives of others in the service of inter subjectivity, can be augmented by simulating emotional experiences through reading literature. The dissertation is divided into six chapters. Chapter One traces the history of the rise of reason and the tum to the self in the West, arguing that the preeminence of reason has pushed emotion to the irrational margins. Chapter two explores theories of emotion, contending that emotion is best understood as a process initiated by affective appraisal. Chapter Two also argues for an understanding of emotion as a necessary part of the process of knowing. Chapter Three uses an Aristotelian analysis of virtue to argue for the need for emotion in developing virtue and sustaining community. Chapter Four sets down a theory of emotional attending that argues against viewing others as objects or as mirror images of the self. Chapter Five offers an argument about the simulation of emotion and the use of the imagination in reading literature, and why expanding emotional resources is an important goal. Chapter Six provides a practical example of how reading can enrich the fund of emotional experiences upon which one draws to attend to the emotional lives of others through analysis of Richard Russo\u27s, Empire Falls

Addressing Distress and Pain in Animal Research: The Veterinary, Research, Societal, Regulatory and Ethical Contexts for Moving Forward

While most people recognize that biomedical scientists are searching for knowledge that will improve the health of humans and animals, the image of someone deliberately causing harm to an animal in order to produce data that may lead to some future benefit has always prompted an uncomfortable reaction outside the laboratory. However, proponents of animal research have usually justified the practice by reference to greater benefits (new knowledge and medical treatments) over lesser costs (in animal suffering and death). Given that one of the costs of animal research is the suffering experienced by the animals, the goal of eliminating distress and pain in the animal laboratory wherever feasible, is one that few, if any, people (especially scientists) would argue against. Moreover, we contend that a laudable, long-range goal would be the elimination of all substantial distress and pain in the animal laboratory (Stephens and Conlee, 2004) although some might see this as an insurmountable technical challenge

Addressing Distress and Pain in Animal Research: The Veterinary, Research, Societal, Regulatory and Ethical Contexts for Moving Forward

While most people recognize that biomedical scientists are searching for knowledge that will improve the health of humans and animals, the image of someone deliberately causing harm to an animal in order to produce data that may lead to some future benefit has always prompted an uncomfortable reaction outside the laboratory. However, proponents of animal research have usually justified the practice by reference to greater benefits (new knowledge and medical treatments) over lesser costs (in animal suffering and death). Given that one of the costs of animal research is the suffering experienced by the animals, the goal of eliminating distress and pain in the animal laboratory wherever feasible, is one that few, if any, people (especially scientists) would argue against. Moreover, we contend that a laudable, long-range goal would be the elimination of all substantial distress and pain in the animal laboratory (Stephens and Conlee, 2004) although some might see this as an insurmountable technical challenge