Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

Anomaly detection in elderly daily behavior in ambient sensing environments

Current ubiquitous computing applications for smart homes aim to enhance people’s daily living respecting age span. Among the target groups of people, elderly are a population eager for “choices for living arrangements”, which would allow them to continue living in their homes but at the same time provide the health care they need. Given the growing elderly population, there is a need for statistical models able to capture the recurring patterns of daily activity life and reason based on this information. We present an analysis of real-life sensor data collected from 40 different households of elderly people, using motion, door and pressure sensors. Our objective is to automatically observe and model the daily behavior of the elderly and detect anomalies that could occur in the sensor data. For this purpose, we first introduce an abstraction layer to create a common ground for home sensor configurations. Next, we build a probabilistic spatio-temporal model to summarize daily behavior. Anomalies are then defined as significant changes from the learned behavioral model and detected using a cross-entropy measure. We have compared the detected anomalies with manually collected annotations and the results show that the presented approach is able to detect significant behavioral changes of the elderly

HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline

Our aging society claims for innovative tools to early detect symptoms of cognitive decline. Several research efforts are being made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect a decline of the cognitive functions of the elderly in order to promptly alert practitioners. Even though those tools may provide accurate predictions, they currently provide limited support to clinicians in making a diagnosis. Indeed, most AI systems do not provide any explanation of the reason why a given prediction was computed. Other systems are based on a set of rules that are easy to interpret by a human. However, those rule-based systems can cope with a limited number of abnormal situations, and are not flexible enough to adapt to different users and contextual situations. In this paper, we tackle this challenging problem by proposing a flexible AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions at a fine-grained level. Our method relies on well known clinical indicators that consider subtle and overt behavioral anomalies, as well as spatial disorientation and wandering behaviors. In order to adapt to different individuals and situations, anomalies are recognized using a collaborative approach. We experimented our approach with a large set of real world subjects, including people with MCI and people with dementia. We also implemented a dashboard to allow clinicians to inspect anomalies together with the explanations of predictions. Results show that our system's predictions are significantly correlated to the person's actual diagnosis. Moreover, a preliminary user study with clinicians suggests that the explanation capabilities of our system are useful to improve the task performance and to increase trust. To the best of our knowledge, this is the first work that explores data-driven explainable AI for supporting the diagnosis of cognitive decline

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

Peer reviewe

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future

A Sensing Platform to Monitor Sleep Efficiency

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency.</p

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Cenários comunicacionais baseados em IOT para a promoção do bem-estar físico, psicológico e social dos séniores

The main objective of this research is to design and validate IoT based social hybrid scenario model that has the potential to promote psychological and physical wellbeing among older adults. The main reason to design and validate the model is age growth, older adults face psychological, physical and social well-being problems that increase mild cognitive impairment and frailty among older adults. Thus, to overcome older adults' problems, the study proposes and validates an IoT-based social hybrid scenario model. The model's features contain passive communication in which Drs, caregivers, and family members can monitor older adults' physical data from long distances. The model's features also contained intentional communication in which Older adults can interact online by text, audio, video calls, sharing images, and online activities such as painting, exercises and cooking. Moreover, older adults can do outdoor activities by inviting peers, friends or family members; the activities can be location-based IoT games, city tours, groups gardening and dinners. The outcomes of model validation will indicate how IoT characteristics can promote physical, psychological and social well-being and provide an opportunity for older adults to spend their life independently. The research that embodies this thesis includes 411 senior Portuguese Universities which are located mainland and on the island of Portugal. Using descriptive research methodology, where quantitative results are analysed, the results indicated a holistic scenario of passive and intentional communication in the context of well-being promotion among olderadults. from here, the social hybrid scenario is outlined, a hybrid model that offers passive and intentional communication between olderadults, family and medical doctors in the context of well-being promotion. The design and characteristics of the model are based on the existing knowledg, and needs of older adults, family members and also medical doctors. Such as model is a compound of passive and intentional characteristics that helps to reduce problem-related mental and physical health. The Passive and intentional communication characteristics are capable to create an environment for older adultsto take care of their psychological and physical health without any intervention and also increase their social physical and online activities, these activities help to promote the well-being of olderadults andd improve the daily lifestyle.O principal objetivo desta pesquisa é projetar e validar um modelo de cenário híbrido social baseado em IoT que tenha o potencial de promover o bem-estar psicológico e físico entre os idosos. A principal razão para projetar e validar o modelo é o crescimento da idade, os idosos enfrentam problemas psicológicos, físicos e de bem-estar social que aumentam o comprometimento cognitivo leve e a fragilidade entre os idosos. Assim, para superar os problemas dos idosos, o estudo propõe e valida um modelo de cenário híbrido social baseado em IoT. Os recursos do modelo contêm comunicação passiva na qual médicos, cuidadores e familiares podem monitorar os dados físicos dos idosos a longas distâncias. As características do modelo também contemplam comunicação intencional em que os idosos podem interagir online por meio de texto, áudio, videochamadas, compartilhamento de imagens e atividades online como pintura, exercícios e culinária. Além disso, os idosos podem fazer atividades ao ar livre convidando colegas, amigos ou familiares; as atividades podem ser jogos de IoT baseados em localização, passeios pela cidade, jardinagem em grupo e jantares. Os resultados da validação do modelo indicam como as características da IoT podem promover o bem-estar físico, psicológico e social e fornecer uma oportunidade para os idosos passarem sua vida de forma independente. A investigação que dá corpo a esta tese inclui 411 universidades portuguesas seniores localizadas no continente e na ilha de Portugal. Utilizando metodologia de pesquisa descritiva, onde são analisados resultados quantitativos, os resultados indicaram um cenário holístico de comunicação passiva e intencional no contexto da promoção do bem-estar entre idosos. a partir daqui, delineia-se o cenário social híbrido, um modelo híbrido que oferece comunicação passiva e intencional entre idosos, médicos de família e médicos no contexto da promoção do bem-estar. O desenho e as características do modelo baseiam-se no conhecimento existente e nas necessidades dos idosos, familiares e também médicos. Tal modelo é um composto de características passivas e intencionais que ajuda a reduzir os problemas relacionados com a saúde mental e física. As características de comunicação passiva e intencional são capazes de criar um ambiente para que os idosos cuidem de sua saúde psicológica e física e também aumentem suas atividades sociais físicas e online, essas atividades ajudam a promover o bem-estar dos idosos e melhorar o estilo de vida diário.Programa Doutoral em Informação e Comunicação em Plataformas Digitai