Low-Cost Indoor Localisation Based on Inertial Sensors, Wi-Fi and Sound

The average life expectancy has been increasing in the last decades, creating the need for new technologies to improve the quality of life of the elderly. In the Ambient Assisted Living scope, indoor location systems emerged as a promising technology capable of sup porting the elderly, providing them a safer environment to live in, and promoting their autonomy. Current indoor location technologies are divided into two categories, depend ing on their need for additional infrastructure. Infrastructure-based solutions require expensive deployment and maintenance. On the other hand, most infrastructure-free systems rely on a single source of information, being highly dependent on its availability. Such systems will hardly be deployed in real-life scenarios, as they cannot handle the absence of their source of information. An efficient solution must, thus, guarantee the continuous indoor positioning of the elderly. This work proposes a new room-level low-cost indoor location algorithm. It relies on three information sources: inertial sensors, to reconstruct users’ trajectories; environ mental sound, to exploit the unique characteristics of each home division; and Wi-Fi, to estimate the distance to the Access Point in the neighbourhood. Two data collection protocols were designed to resemble a real living scenario, and a data processing stage was applied to the collected data. Then, each source was used to train individual Ma chine Learning (including Deep Learning) algorithms to identify room-level positions. As each source provides different information to the classification, the data were merged to produce a more robust localization. Three data fusion approaches (input-level, early, and late fusion) were implemented for this goal, providing a final output containing complementary contributions from all data sources. Experimental results show that the performance improved when more than one source was used, attaining a weighted F1-score of 81.8% in the localization between seven home divisions. In conclusion, the evaluation of the developed algorithm shows that it can achieve accurate room-level indoor localization, being, thus, suitable to be applied in Ambient Assisted Living scenarios.O aumento da esperança média de vida nas últimas décadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos. No âmbito da Assistência à Autonomia no Domicílio, sistemas de localização indoor têm emergido como uma tecnologia promissora capaz de acompanhar os idosos e as suas atividades, proporcionando-lhes um ambiente seguro e promovendo a sua autonomia. As tecnologias de localização indoor atuais podem ser divididas em duas categorias, aquelas que necessitam de infrastruturas adicionais e aquelas que não. Sistemas dependentes de infrastrutura necessitam de implementação e manutenção que são muitas vezes dispendiosas. Por outro lado, a maioria das soluções que não requerem infrastrutura, dependem de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que não consegue lidar com a falta de informação de um sensor dificilmente será implementado em cenários reais. Uma solução eficiente deverá assim garantir o acompanhamento contínuo dos idosos. A solução proposta consiste no desenvolvimento de um algoritmo de localização indoor de baixo custo, baseando-se nas seguintes fontes de informação: sensores inerciais, capazes de reconstruir a trajetória do utilizador; som, explorando as características dis tintas de cada divisão da casa; e Wi-Fi, responsável pela estimativa da distância entre o ponto de acesso e o smartphone. Cada fonte sensorial, extraída dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada através de algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas fontes contêm informação diferente acerca das mesmas características do sistema, a sua fusão torna a classificação mais informada e robusta. Com este objetivo, foram implementadas três abordagens de fusão de dados (input data, early and late fusion), fornecendo um resultado final derivado de contribuições complementares de todas as fontes de dados. Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido melhorou com a inclusão de informação multi-sensor, alcançando um valor para F1- score de 81.8% na distinção entre sete divisões domésticas. Concluindo, o algoritmo de localização indoor, combinando informações de três fontes diferentes através de métodos de fusão de dados, alcançou uma localização room-level e está apto para ser aplicado num cenário de Assistência à Autonomia no Domicílio

SHELDON Smart habitat for the elderly.

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

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

ARCH 14 - International Conference on Research on Health Care Architecture - November 19-21, 2014, Espoo, Finland - Conference Proceedings

Healthcare Architecture has grown rapidly in recent years. However, there are still many questions remaining. The commission, therefore, is to share the existing research knowledge and latest results and to carry out research projects focusing more specifically on the health care situation in a variety of contexts. The ARCH14 conference was the third conference in the series of ARCH conferences on Research on Health Care Architecture initiated by Chalmers University. It was realized in collaboration with the Nordic Research Network for Healthcare Architecture .It was a joint event between Aalto University, Finnish Institute of Occupational Health (FIOH) and National Institute of Health and Welfare (THL International).The conference gathered together more than 70 researchers and practitioners from across disciplines and countries to discuss the current themes

Orthogeriatrics

This new open access edition supported by the Fragility Fracture Network aims at giving the widest possible dissemination on fragility fracture (especially hip fracture) management and notably in countries where this expertise is sorely needed. It has been extensively revised and updated by the experts of this network to provide a unique and reliable content in one single volume. Throughout the book, attention is given to the difficult question of how to provide best practice in countries where the discipline of geriatric medicine is not well established and resources for secondary prevention are scarce. The revised and updated chapters on the epidemiology of hip fractures, osteoporosis, sarcopenia, surgery, anaesthesia, medical management of frailty, peri-operative complications, rehabilitation and nursing are supplemented by six new chapters. These include an overview of the multidisciplinary approach to fragility fractures and new contributions on pre-hospital care, treatment in the emergency room, falls prevention, nutrition and systems for audit. The reader will have an exhaustive overview and will gain essential, practical knowledge on how best to manage fractures in elderly patients and how to develop clinical systems that do so reliably

Towards a Sustainable Life: Smart and Green Design in Buildings and Community

This Special Issue includes contributions about occupants’ sustainable living in buildings and communities, highlighting issues surrounding the sustainable development of our environments and lives by emphasizing smart and green design perspectives. This Special Issue specifically focuses on research and case studies that develop promising methods for the sustainable development of our environment and identify factors critical to the application of a sustainable paradigm for quality of life from a user-oriented perspective. After a rigorous review of the submissions by experts, fourteen articles concerning sustainable living and development are published in this Special Issue, written by authors sharing their expertise and approaches to the concept and application of sustainability in their fields. The fourteen contributions to this special issue can be categorized into four groups, depending on the issues that they address. All the proposed methods, models, and applications in these studies contribute to the current understanding of the adoption of the sustainability paradigm and are likely to inspire further research addressing the challenges of constructing sustainable buildings and communities resulting in a sustainable life for all of society