Progress in ambient assisted systems for independent living by the elderly

One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expectancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients’ place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost-effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end-to-end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real-time monitoring of the environment and occupant behavior using an event-driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objectives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks while the opportunities for integrating environmental factors for analytics and decision-making, in particular for the long-term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio-cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Events of daily living classification on an ambient assisted living environment

Dissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresPopulation ageing is a global demographic challenge and countries all around the world are facing significant pressure on their health and social care systems in order to mitigate the effects of it. The emerging social aspect introduced some crucial challenges to society and greater demands on the actual health care sector, which led to the emergence and increased integration of agefriendly innovative welfare technological-based care services for safe and independent ageing, including the assisted living technologies based on Ambient Intelligence (AmI) paradigm and Pervasive HealthCare. The Ambient Assisted Living (AAL) systems intend to provide caregivers with a detailed overview of their Events of Daily Living (EDL), which constitutes a clinical criteria to evaluate activity limitations. This dissertation addresses these challenges and contributes to the Ambient Assisted Living research, by means of a holistic solution composed of a beyond the state-of-the-art AAL technologies, representing a novel approach to assist in the investigation and on the modeling of a subset of Events of Daily Living (EDL), for sustaining independent living and a continual naturalistic assessment of health. The investigation was focused on 1) developing a multisensorial pervasive Research Data Acquistion (RDA) Platform with embedded Ambient Intelligence (AmI), 2) COTS to verify their validity and reliability for healthcare applications. The proposed solution has been thoroughly evaluated in the Ambient Assisted Living Laboratory that showed its effectiveness classifying EDL through the application of the AAL paradigm in the real world.O envelhecimento populacional é um desafio demográfico global e os países em todo o mundo estão sob com enorme pressão nos seus sistemas de saúde a fim de mitigar os efeitos que poderão advir. O aspecto social emergente introduziu alguns desafios cruciais para a sociedade e uma maior sobrecarga no setor de saúde, o que levou ao surgimento e aumento da integração de serviços inovadores de assistência social, de modo a que haja um envelhecimento seguro e independente, incluindo as tecnologias de assistência à vida com base no paradigma de Ambient Intelligence (AmI) e no Pervasive HealthCare, os sistemas de Ambient Assisted Living (AAL). Eles pretendem fornecer aos profissionais de saúde uma visão detalhada de seu Events of Daily Living (EDL), que constitui um critério clínico para avaliar as limitações da atividade. Para enfrentar estes desafios, esta dissertação contribui para a pesquisa na área de Ambient Assisted Living, por meio de uma solução holística composta por uma tecnologia além das tecnologias state-of-the-art, representando uma nova abordagem para auxiliar na investigação e na modelação de um subconjunto de Events of Daily Living (EDL), para sustentar uma vida independente e uma avaliação naturalística contínua da saúde. A investigação foi focada em 1) desenvolver uma plataforma multisensorial pervasiva Research Data Acquistion (RDA) com Ambient Intelligence (AmI), 2) COTS para verificar a sua validade e fiabilidade para aplicações de assistência médica. A solução proposta foi avaliada no Ambient Assisted Living Laboratory, que mostrou bastante eficácia ao classificar EDL através da aplicação do paradigma AAL no mundo real

Information and communication technology solutions for outdoor navigation in dementia

INTRODUCTION: Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities? METHODS: Review of literature and cross-disciplinary expert discussion. RESULTS: A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs. DISCUSSION: The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Discovering behavioural patterns using conversational technology for in-home health and well-being monitoring

Advancements in conversational AI have created unparalleled opportunities to promote the independence and well-being of older adults, including people living with dementia (PLWD). However, conversational agents have yet to demonstrate a direct impact in supporting target populations at home, particularly with long-term user benefits and clinical utility. We introduce an infrastructure fusing in-home activity data captured by Internet of Things (IoT) technologies with voice interactions using conversational technology (Amazon Alexa). We collect 3103 person-days of voice and environmental data across 14 households with PLWD to identify behavioural patterns. Interactions include an automated well-being questionnaire and 10 topics of interest, identified using topic modelling. Although a significant decrease in conversational technology usage was observed after the novelty phase across the cohort, steady state data acquisition for modelling was sustained. We analyse household activity sequences preceding or following Alexa interactions through pairwise similarity and clustering methods. Our analysis demonstrates the capability to identify individual behavioural patterns, changes in those patterns and the corresponding time periods. We further report that households with PLWD continued using Alexa following clinical events (e.g., hospitalisations), which offers a compelling opportunity for proactive health and well-being data gathering related to medical changes. Results demonstrate the promise of conversational AI in digital health monitoring for ageing and dementia support and offer a basis for tracking health and deterioration as indicated by household activity, which can inform healthcare professionals and relevant stakeholders for timely interventions. Future work will use the bespoke behavioural patterns extracted to create more personalised AI conversations

Ambient assisted living systems for older people with Alzheimer’s

The older people population in the world is increasing as a result of advances in technology, public health, nutrition and medicine. People aged sixty or over were more than 11.5% of the global population in 2012. By 2050, this percentage is expected to be doubled to two billion and around thirty-three countries will have more than ten million people aged sixty or more each. With increasing population age around the word, medical and everyday support for the older people, especially those who live with Alzheimer’s who can't be trusted for consistence interaction with their environment, attract the attention of scientists and health care providers. Existing provisions are often deemed inadequate; e.g.; current UK housing services for the older people are inadequate for an aging population both in terms of quality and quantity. Many older people prefer to spend their remaining life in their home environment; over 40% of the older people have concerns about having to move into a care home when they become old and nearly 70% of them worry about losing their independence or becoming dependent on others. There is, therefore, a growing interest in the design and implementation of smart and intelligent Ambient Assisted Living (AAL) systems that can provide everyday support to enable the older people to live independently in their homes. Moreover, such systems will reduce the cost of health care that governments have to tackle in providing assistance for this category of citizens. It also relieves relatives from continuous and often tedious supervision of these people around the clock, so that their life and commitments are not severely affected. Hence, recognition, categorization, and decision-making for such peoples’ everyday life activities is very important to the design of proper and effective intelligent support systems that are able to provide the necessary help for them in the right manner and time. Consequently, the collection of monitoring data for such people around the clock to record their vital signs, environmental conditions, health condition, and activities is the entry level to design such systems. This study aims to capture everyday activities using ambient sensory II information and proposes an intelligent decision support system for older people living with Alzheimer’s through conducting field study research in the Kingdom of Saudi Arabia within their homes and health care centres. The study considers the older people, who live with Alzheimer’s in Kingdom of Saudi Arabia. Since Alzheimer’s is a special form of dementia that can be supported in early stages with the ambient assistive systems. Further, the results of the field study can also be generalized to societies, which are interested in the mental and cognitive behaviour of older people. This generalization is related to the existence of common similarities in their daily life. Moreover, the approach is a generalized approach. Hence it can also be utilized on a new society which is conducting the same field study. This study initially presents a real-life observation process to identify the most common activities for these patients’ group. Then, a survey analysis is carried out to identify the daily life activities based on the observation. The survey analysis is accomplished using a U-test (Mann-Whitney). According to the analysis, it has been found that these people have fourteen common activities. However, three of these activities such as sleeping, walking (standing) and sitting cover about 72% of overall activities. Therefore, this study focuses on the recognition of these three common activities to demonstrate the effectiveness of the research. The activity recognition is carried out using a common image processing technique, called Phase-Correlation and Log-Polar (PCLP) transformation. According to results, the techniques predicted human activities of about 43.7%. However, this ratio is low to utilise for further analysis. Therefore, an Artificial Neural Network (ANN)- based PCLP model is developed to increase the accuracy of activity recognition. The enhanced PCLP transformation method can predict nearly 80% of the evaluated activities. Moreover, this study also presents a decision support system for Alzheimer’s people, which will provide these people with a safe environment. The decision support system utilises an extended sensory-based system, including a vision sensor, vital signs sensor and environmental sensor with expert rules. The proposed system was implemented on an older people patient with 87.2% accuracy