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Affective state estimation is a research field that has gained increased attention from the research community in the last decade. Two of the main catalysts for this are the advancement in the data analysis using artificial intelligence and the availability of high-quality video. Unfortunately, benchmarks and public datasets are limited, thus making the development of new methodologies and the implementation of comparative studies essential. The current work presents the eSEE-d database, which is a resource to be used for emotional State Estimation based on Eye-tracking data. Eye movements of 48 participants were recorded as they watched 10 emotion-evoking videos, each of them followed by a neutral video. Participants rated four emotions (tenderness, anger, disgust, sadness) on a scale from 0 to 10, which was later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled three self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires' self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low-level eye-recorded metrics, and their correlations with the participants' ratings are investigated. Finally, we take on the challenge to classify arousal and valence levels based solely on eye and gaze features, leading to promising results. In particular, the Deep Multilayer Perceptron (DMLP) network we developed achieved an accuracy of 92% in distinguishing positive valence from non-positive and 81% in distinguishing low arousal from medium arousal. The dataset is made publicly available. [ABSTRACT FROM AUTHOR]

In this review, we focus on the various integrated care models that have been applied for the management of dementia patients. We explore the different types of assistive technologies (mobile, wearable, and home-based systems) for dementia care, with a special emphasis on technologies that involve or target the informal caregiver as end user. In an attempt to reveal the needs for information sharing, communication, and collaboration between people with dementia and caregivers involved in the effective and integrated management of the disease, we analyze the trends in research and development to date, we seek to understand and reflect upon the state of the art in assistive technologies for dementia, and we highlight domains that appear underexplored, in order to guide future research. We also explore the cost effectiveness of such technologies and integrated care models for the management of dementia patients and comment on current limitations and future trends and directions. Findings indicate the urgent need and the current lack of a comprehensive and cost-effective solution that will incorporate information system technologies for the provision of integrated care services to dementia patients and their informal caregivers. [ABSTRACT FROM AUTHOR]

Personalized medicine should target not only the genetic and clinical aspects of the individual patients but also the different cognitive, psychological, family and social factors involved in various clinical choices. To this direction, in this paper, we present instruments to assess the psycho-emotional status of cancer patients and to evaluate the resilience in their family constructing in such a way an augmented patient profile. Using this profile, 1) information provision can be tailored according to patients characteristics; 2) areas of functioning can be monitored both by the patient and by the clinicians, providing suggestions and alerts; 3) personalized decision aids can be develop to increase patient's participation in the consultation process with their physicians and improve their satisfaction and involvement in the decision-making process. Our preliminary evaluation shows promising results and the potential benefits of the tools. [ABSTRACT FROM AUTHOR]

Information in the healthcare domain and in particular personal health record information is heterogeneous by nature. Clinical, lifestyle, environmental data and personal preferences are stored and managed within such platforms. As a result, significant information from such diverse data is difficult to be delivered, especially to non-IT users like patients, physicians or managers. Another issue related to the management and analysis is the volume, which increases more and more making the need for efficient data visualization and analysis methods mandatory. The objective of this work is to present the architectural design for seamless integration and intelligent analysis of distributed and heterogeneous clinical information in the PHR context, as a result of a requirements elicitation process in iManageCancer project. This systemic approach aims to assist health-care professionals to orient themselves in the disperse information space and enhance their decision-making capabilities, to encourage patients to have an active role by managing their health information and interacting with health-care professionals. [ABSTRACT FROM AUTHOR]

Perceiving and identifying emotions on facial expressions is one of the basic abilities that compose emotional intelligence, and is crucial for normal social functions. It is well documented that facial expression conveys information about felt emotion, and that expressive behavior can activate or regulate the emotion required by a given situation. Instruments measuring emotion perception based on facial expression have been found in literature either as stand-alone scales or as part of other tests. The proposed tool expands existing instruments to combine online availability while affording assessment of emotion recognition on a continuum of intensity. It was founded on Ekman's Facial Action Units, with two Virtual Characters (male and female) portraying five basic emotions Anger, Disgust, Fear, Joy, Sadness, plus Neutral expression. The user can navigate on the custom-made pentagon and choose the emotion and intensity level (1-5) through a single click. The preliminary evaluation of the tool on thirty normal subjects provided threshold data that can later be used as benchmarks to assess emotion perception sensitivity in psychiatric disorders such as depression and schizophrenia characterized by emotional dysfunction. [ABSTRACT FROM AUTHOR]

In the last decade, clinicians have started to shift from an individualistic perspective of the patient towards family-centred models of care, due to the increasing evidence from research and clinical practice of the crucial role of significant others in determining the patient's adjustment to cancer disease and management. eHealth tools can be considered a means to compensate the services gap and support outpatient care flows. Within the works of the European H2020 iManageCancer project, a review of the literature in the field of family resilience was conducted, in order to determine how to monitor the patient and his/her family's resilience through an eHealth platform. An analysis of existing family resilience questionnaires suggested that no measure was appropriate for cancer patients and their families. For this reason, a new family resilience questionnaire (named FaRe) was developed to screen the patient's and caregiver's psycho-emotional resources. Composed of 24 items, it is divided into four subscales: Communication and Cohesion, Perceived Family Coping, Religiousness and Spirituality, and Perceived Social Support. Embedded in the iManageCancer eHealth platform, it allows users and clinicians to monitor the patient's and the caregivers' resilience throughout the cancer trajectory. [ABSTRACT FROM AUTHOR]

Nowadays, patients have a wealth of information available on the Internet. Despite the potential benefits of Internet health information seeking, several concerns have been raised about the quality of information and about the patient's capability to evaluate medical information and to relate it to their own disease and treatment. As such, novel tools are required to effectively guide patients and provide high-quality medical information in an intelligent and personalised manner. With this aim, this paper presents the Personal Health Information Recommender (PHIR), a system to empower patients by enabling them to search in a high-quality document repository selected by experts, avoiding the information overload of the Internet. In addition, the information provided to the patients is personalised, based on individual preferences, medical conditions and other profiling information. Despite the generality of our approach, we apply the PHIR to a personal health record system constructed for cancer patients and we report on the design, the implementation and a preliminary validation of the platform. To the best of our knowledge, our platform is the only one combining natural language processing, ontologies and personal information to offer a unique user experience. [ABSTRACT FROM AUTHOR]