Assessment of ambient assisted living systems for patients with mild cognitive impairment

According to the World Health Organization, about 50 million people worldwide suffer from dementia. Ten million new cases added every year. Mild Cognitive Impairment (MCI) affects more than 15% of the population aged 65. Technological solutions, such as smart home technology with ubiquitous computing devices, 24/7 telemedical observation and support can alleviate the growing problem and lower pressure on the healthcare system. This approach is also preferable for homecare patients in distant and rural areas. MCI patients are mostly home-based. Ambient Assisted Living (AAL) systems provide tools for automatic registration of vital signs and other medically and socially important information. AAL system for MCI patients is a logical answer to the problem. At the same time, many of the proposed AAL systems are proprietary, technically complicated and have a high price tag for implementation and service. Also, some proposed technical solutions not entirely reflect the opinion of healthcare stakeholders. The current study was proposed as a way to bridge the possible differences in the positions. An online anonymous questionnaire for healthcare professionals was created to prove or disprove the number of interconnected hypotheses about the necessity and feasibility of AAL system for MCI patients. The main focus was made on the hypotheses: "There is necessity of AAL systems for the healthcare" and "AAL systems are capable of providing assistance for patients with Mild Cognitive Impairment". The questionnaire was presented to more than three hundred potential respondents. Around a hundred and twenty agreed to fill it, and sixty completed the whole questionnaire. Results were analyzed to produce some directions guideline for future technical applications of AAL systems for MCI patients and future research. Descriptive statistics show support for the implementation of general AAL and variants for MCI patients. Comparative analysis of ordinal data for specific groups of respondents is done with help of non-parametric tests. Mann–Whitney–Wilcoxon test and Kruskal-Wallis test are applied. Table questions results are analyzed with chisquare for frequency tables. Group analysis demonstrated relative positive uniformity in of responses in the support of AAL of MCI patients.Segundo a Organização Mundial da Saúde, cerca de 50 milhões de pessoas em todo o mundo sofrem de demência. Dez milhões de novos casos adicionados a cada ano. O comprometimento cognitivo leve (MCI) afeta mais de 15% da população com 65 anos. Soluções tecnológicas, como tecnologia de casa inteligente com dispositivos de computação onipresentes, observação e suporte telemédico 24 horas por dia, 7 dias por semana, podem aliviar o problema crescente e diminuir a pressão sobre o sistema de saúde. Essa abordagem também é preferível para pacientes de cuidados domiciliares em áreas distantes e rurais. Os pacientes com CCL são, em sua maioria, domiciliares. Os sistemas Ambient Assisted Living (AAL) fornecem ferramentas para registro automático de sinais vitais e outras informações médicas e socialmente importantes. O sistema AAL para pacientes com MCI é uma resposta lógica para o problema. Ao mesmo tempo, muitos dos sistemas AAL propostos são proprietários, tecnicamente complicados e têm um alto preço para implementação e serviço. Além disso, algumas soluções técnicas propostas não refletem inteiramente a opinião das partes interessadas na área da saúde. O presente estudo foi proposto como forma de colmatar as possíveis diferenças nas posições. Um questionário anônimo online para profissionais de saúde foi criado para comprovar ou refutar o número de hipóteses interligadas sobre a necessidade e viabilidade do sistema AAL para pacientes com CCL. O foco principal foi feito nas hipóteses: "Há necessidade de sistemas de AAL para a saúde" e "Os sistemas de AAL são capazes de prestar assistência a pacientes com Comprometimento Cognitivo Leve". O questionário foi apresentado a mais de trezentos respondentes potenciais. Cerca de cento e vinte concordaram em preenchê-lo e sessenta preencheram todo o questionário. Os resultados foram analisados para produzir algumas diretrizes para futuras aplicações técnicas de sistemas AAL para pacientes com MCI e pesquisas futuras. Estatísticas descritivas mostram suporte para a implementação de AAL geral e variantes para pacientes com CCL. A análise comparativa de dados ordinais para grupos específicos de respondentes é feita com a ajuda de testes não paramétricos. Aplicam-se os testes de Mann-Whitney-Wilcoxon e Kruskal-Wallis. Os resultados das questões da tabela são analisados com qui-quadrado para tabelas de frequência. A análise do grupo demonstrou relativa uniformidade positiva nas respostas no suporte de AAL de pacientes com CCL.Selon l'Organisation mondiale de la santé, environ 50 millions de personnes dans le monde souffrent de démence. Dix millions de nouveaux cas ajoutés chaque année. Les troubles cognitifs légers (MCI) touchent plus de 15 % de la population âgée de 65 ans. Les solutions technologiques, telles que la technologie de la maison intelligente avec des appareils informatiques omniprésents, l'observation et le soutien télémédicaux 24 heures sur 24, 7 jours sur 7, peuvent atténuer le problème croissant et réduire la pression sur le système de santé. Cette approche est également préférable pour les patients en soins à domicile dans les régions éloignées et rurales. Les patients MCI sont pour la plupart à domicile. Les systèmes Ambient Assisted Living (AAL) fournissent des outils pour l'enregistrement automatique des signes vitaux et d'autres informations importantes sur le plan médical et social. Le système AAL pour les patients MCI est une réponse logique au problème. Dans le même temps, bon nombre des systèmes AAL proposés sont propriétaires, techniquement compliqués et ont un prix élevé pour la mise en oeuvre et le service. De plus, certaines solutions techniques proposées ne reflètent pas entièrement l'opinion des acteurs de santé. L'étude actuelle a été proposée comme un moyen de combler les différences possible dans les positions. Un questionnaire anonyme en ligne destiné aux professionnels de la santé a été créé pour prouver ou réfuter le nombre d'hypothèses interconnectées sur la nécessité et la faisabilité du système AAL pour les patients MCI. L'accent a été mis principalement sur les hypothèses: "Il existe une nécessité de systèmes AAL pour les soins de santé" et "Les systèmes AAL sont capables de fournir une assistance aux patients atteints de troubles cognitifs légers". Le questionnaire a été présenté à plus de trois cents répondants potentiels. Environ cent vingt ont accepté de le remplir, et soixante ont rempli tout le questionnaire. Les résultats ont été analysés pour produire des lignes directrices pour les futures applications techniques des systèmes AAL pour les patients MCI et l'avenir de la recherche. Les statistiques descriptives montrent un soutien à la mise en oeuvre de l'AAL général et des variantes pour les patients MCI. L'analyse comparative des données ordinales pour des groupes spécifiques de répondants est effectuée à l'aide de tests non paramétriques. Le test de Mann-Whitney-Wilcoxon et le test de Kruskal-Wallis sont appliqués. Les résultats des questions de tableau sont analysés avec le chi carré pour les tableaux de fréquence. L'analyse de groupe a démontré une uniformité positive relative dans les réponses à l'appui de l'AAL des patients MCI

Physiological and behavior monitoring systems for smart healthcare environments: a review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressedinfo:eu-repo/semantics/publishedVersio

Smart and Pervasive Healthcare

Smart and pervasive healthcare aims at facilitating better healthcare access, provision, and delivery by overcoming spatial and temporal barriers. It represents a shift toward understanding what patients and clinicians really need when placed within a specific context, where traditional face-to-face encounters may not be possible or sufficient. As such, technological innovation is a necessary facilitating conduit. This book is a collection of chapters written by prominent researchers and academics worldwide that provide insights into the design and adoption of new platforms in smart and pervasive healthcare. With the COVID-19 pandemic necessitating changes to the traditional model of healthcare access and its delivery around the world, this book is a timely contribution

Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

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

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain