Smart kitchen for Ambient Assisted Living

El envejecimiento de la población es una realidad en todos los países desarrollados. Las predicciones de crecimiento de esta población son alarmantes, planteando un reto para los servicios sociales y sanitarios. Las personas ancianas padecen diversas discapacidades que se van acentuando con la edad, siendo más propensas a sufrir accidentes domésticos, presentando problemas para realizar tareas cotidianas, etc. Esta situación conlleva a una pérdida paulatina de capacidades que en muchas ocasiones acaba con la vida autónoma de la persona. En este contexto, las Tecnologías de la Información y Comunicación (TIC) aplicadas al entorno doméstico pueden jugar un papel importante, permitiendo que las personas ancianas vivan más tiempo, de forma independiente en su propio hogar, presentando, por tanto, una alternativa a la hospitalización o institucionalización de las mismas. Este trabajo da un paso más en este sentido, presentando el diseño y desarrollo de un Ambiente Inteligente en la cocina, que ayuda a las personas ancianas y/o con discapacidad a desempeñar sus actividades de la vida diaria de una forma más fácil y sencilla. Esta tesis realiza sus principales aportaciones en dos campos: El metodológico y el tecnológico. Por un lado se presenta una metodología sistemática para extraer necesidades de colectivos específicos a fin de mejorar la información disponible por el equipo de diseño del producto, servicio o sistema. Esta metodología se basa en el estudio de la interacción Hombre-Máquina en base a los paradigmas y modelos existentes y el modelado y descripción de las capacidades del usuario en la misma utilizado el lenguaje estandarizado propuesto en la Clasificación Internacional del Funcionamiento, de la Discapacidad y de la Salud (CIF). Adicionalmente, se plantea el problema de la evaluación tecnológica, diseñando la metodología de evaluación de la tecnología con la finalidad de conocer su accesibilidad, funcionalidad y usabilidad del sistema desarrollado y aplicándola a 61 usuarios y 31 profesionales de la gerontología. Desde un punto de vista técnico, se afronta el diseño de un ambiente asistido inteligente (Ambient Assisted Living, AAL) en la cocina, planteando y definiendo la arquitectura del sistema. Esta arquitectura, basada en OSGi (Open Services Gateway initiative), oferta un sistema modular, con altas capacidades de interoperabilidad y escalabilidad. Además, se diseña e implementa una red de sensores distribuida en el entorno con el fin de obtener la mayor información posible del contexto, presentando distintos algoritmos para obtener información de alto nivel: detección de caídas o localización. Todos los dispositivos presentes en el entorno han sido modelados utilizando la taxonomía propuesta en OSGi4AmI, extendiendo la misma a los electrodomésticos más habituales de la cocina. Finalmente, se presenta el diseño e implementación de la inteligencia del sistema, que en función de la información procedente del contexto y de las capacidades del usuario da soporte a las principales actividades de la vida diaria (AVD) en la cocina

Holistic System Design for Distributed National eHealth Services

publishedVersio

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

Intelligent data processing to support self-management and responsive care

This research is situated in the area of ambient intelligent systems for assisted living. The motivation for the research was to understand how ambient intelligent systems could be used to support people with learning disabilities in providing more personalised care, as well as function as an aid to support independent living. In the first phase of the research a series of interviews conducted with formal carers of people with learning disabilities highlighted kitchen activities as a potential area of support. This provided a focal point for the research whereby subsequent research involved the development of sensor data mining techniques and machine learning methods to recognise specific meal-preparation activities with a view to supporting task prompting. The goal of task prompting is to enable automated intervention for service users performing meal-preparation activities by tracking the activity in real-time by analysing ambient sensor data. In the second phase of the research a public smart-home dataset was used to develop a novel methodology which uses "temporal clusters" of sensor events as a pre-processing step for extracting features from the data and creating visualisations. In the third phase of the research, a data set comprising different meal-preparation activities undertaken by three participants in a shared kitchen was collected over a period of 8 weeks. This fully annotated dataset includes a combination of data from a range of ambient smart-home sensors and low-resolution thermal cameras. This dataset was used to experiment with knowledge-driven activity recognition techniques, which were used to develop a novel hybrid offline-online learning methodology for real-time activity recognition and prediction. This methodology is shown to overcome the shortcomings of existing supervised activity recognition methods, which require re-training with new data if the activity changes. The new methodology has been designed to enable learning from the user in order to track meal-preparation activities in real-time, detect deviations from the activity, and adapt to changes in the user's performance without requiring re-training. The research presented in this thesis, together with the meal-preparation dataset, are a crucial stepping-stone for the development of future technologies that offer the potential for real-time task prompting and thus could be useful in supporting people with learning disabilities in performing activities more independently. The approaches developed can also generate information that could help carers better understand how their service users are able to perform these activities and hence personalise and adapt the support they provide

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

The case for investment in technology to manage the global costs of dementia

Worldwide growth in the number of people living with dementia will continue over the coming decades and is already putting pressure on health and care systems, both formal and informal, and on costs, both public and private. One response could be to make greater use of digital and other technologies to try to improve outcomes and contain costs. We were commissioned to examine the economic case for accelerated investment in technology that could, over time, deliver savings on the overall cost of care for people with dementia. Our short study included a rapid review of international evidence on effectiveness and cost-effectiveness of technology, consideration of the conditions for its successful adoption, and liaison with people from industry, government, academic, third sector and other sectors, and people with dementia and carers. We used modelling analyses to examine the economic case, using the UK as context. We then discussed the roles that state investment or action could play, perhaps to accelerate use of technology so as to deliver both wellbeing and economic benefits

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000