A people-oriented paradigm for smart cities

Most works in the literature agree on considering the Internet of Things (IoT) as the base technology to collect information related to smart cities. This information is usually offered as open data for its analysis, and to elaborate statistics or provide services which improve the management of the city, making it more efficient and more comfortable to live in. However, it is not possible to actually improve the quality of life of smart cities’ inhabitants if there is no direct information about them and their experiences. To address this problem, we propose using a social and mobile computation model, called the Internet of People (IoP) which empowers smartphones to recollect information about their users, analyze it to obtain knowledge about their habits, and provide this knowledge as a service creating a collaborative information network. Combining IoT and IoP, we allow the smart city to dynamically adapt its services to the needs of its citizens, promoting their welfare as the main objective of the city.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

El papel de los ciudadanos en las ciudades inteligentes: un escenario de movilidad urbana

Gran parte de los esfuerzos dedicados al desarrollo de las llamadas ciudades inteligentes se centran en el campo del Internet of Things (IoT). Las instituciones ofrecen habitualmente la información recolectada mediante IoT en forma de datos abiertos y estadísticas, a partir de las cuales se pueden realizar análisis y obtener conclusiones que ayuden a mejorar la gestión de las ciudades, haciéndolas más eficientes y habitables. No obstante, sin el concurso de los ciudadanos en la generación y recogida de información, no es posible ofrecer una imagen completa de las ciudades. El análisis de la información recopilada no tendrá en cuenta el contexto de las personas, ni podrá adaptarse a las necesidades de las mismas. Para resolver este problema, proponemos el uso de un nuevo modelo capaz de convivir con el de IoT actual y que cubra estas necesidades respecto a los ciudadanos. Se trata de Internet of People (IoP), un modelo de computación social y móvil que permite recopilar información a partir de los smartphones y del uso que hacen de ellos sus propietarios. Mediante un motor de inferencia, dicha información se transforma en conocimiento de los hábitos del usuario del teléfono, conocimiento que puede ser ofrecido a su vez como un servicio. La combinación de los datos recogidos por ambas partes, IoT e IoP, procurará realmente el adjetivo inteligente a la ciudad, permitiendo que los servicios que el IoT ofrece puedan adaptarse a cada persona, y convirtiendo a estas últimas en el objetivo central de la ciudad inteligente.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Using GPS technologies with People with Dementia: A synthesising review and recommendations for future practice

In Norway and other Nordic countries, there is a policy emphasis on using welfare technologies to support people to live at home. For example, Global Positioning Systems (GPS) or ‘location technologies’ are used to support people with dementia and their next of kin with wayfinding. However, the research evidence has not been synthesised, and so the opportunities and challenges presented when using GPS technologies are not clear. This synthesising review examined all available empirical evidence on the use of GPS technologies by people with dementia and their family carers, through a critical disability lens – that is, in terms of protecting a person’s right to live in the community and taking the standpoint of the person with dementia (rather than a caregiver or health professional). Employing this lens meant that we engaged with the literature in a more critical way than standard reviews, and consciously looked for evidence of marginalisation. A search of six major English language databases in 2016 identified 23 studies that met the inclusion criteria. Synthesis of the findings led to the identification of three overarching themes: using GPS to stay safe, taking control and the value of GPS data for researchers. The review revealed a growing interest in the use of GPS technologies by people with dementia, which indicates that policy implementation is effective. Future work should take a disability-rights approach and focus on the value of using GPS technologies from the perspective of the person with dementia, as the opinions of this group are often overlooked in discussions about welfare technologies

Tangibot: A tangible-mediated robot to support cognitive games for ageing people A usability study

[EN] The ever increasing elderly population requires a revision of technology to make it usable and meaningful for them. Most applications take into account their reduced physical and cognitive abilities in order to provide assistive services, but this paper focuses on building technology to improve these capacities through cognitive games. We present Tangibot, a tangible-mediated robot aimed at enabling more intuitive and appealing interactions. A usability study conducted on subjects at three different levels of cognitive impairment (none, mild, and severe) reveals that it is usable and engaging for users with no or mild cognitive impairment, and even though it is less usable for persons with severe impairment, it triggers positive emotional reactions among them, which makes it promising for their use in therapeutic activities.This work is supported by Spanish Ministry of Economy and Competitiveness and funded by the European Development Regional Fund (EDRF-FEDER) with Project TIN2014-60077-R. It is also supported by fellowship ACIF/2014/214 within the VALi+d program from Conselleria d'Educacio, Cultura i Esport (Generalitat Valenciana), and by fellowship FPU14/00136 within the FPU program from Spanish Ministry of Education, Culture, and Sport.García Sanjuan, F.; Jaén Martínez, FJ.; Nácher-Soler, VE. (2017). Tangibot: A tangible-mediated robot to support cognitive games for ageing people A usability study. Pervasive and Mobile Computing. 34:91-105. doi:10.1016/j.pmcj.2016.08.007S911053

Vehicle Category Classification Based on GPS Trajectory Data

Understanding the category of a vehicle is an essential study for transportation safety and operation. With the explosive number of GPS devices, there are massive vehicle GPS trajectory data sets whose sizes are beyond the traditional trajectory analysis method's capability. This study utilizes Apache Spark™ to build up a framework whose output data can be compatible with machine learning algorithms for vehicle category classification. Five types of features were extracted from the GPS trajectory data, namely driving habits statistics, trajectory sample quality statistics, geographical information statistics, origin and destination cluster statistics, and temporal statistics. The spatial clustering algorithm and spatial join are incorporated in the workflow, significantly broadening the number of features for the training data set. The results show that the five types of statistics extracted from the trajectory are adequate for distinguishing different vehicle categories by machine learning algorithms. The same accuracy rank sequence for the vehicle classes was observed across different types of features and algorithms, and the decision tree ensemble algorithms have better performance over the logistic regression and support vector machine algorithms

Behavioural modelling for ambient assisted living

Tese de doutoramento - MAP-i (University of Minho, Aveiro, and Porto)A mudança incomum na rotina diária ao nível da mobilidade de um idoso em sua casa, pode ser um sinal ou sintoma precoce para a possibilidade de vir a desenvolver um problema de saúde. O recurso a diferentes sensores pode ser um meio para complementar os sistemas de cuidados de saúde tradicionais, de forma a obter uma visão mais detalhada da mobilidade diária do individuo em sua casa, enquanto realiza as suas tarefas diárias. Acreditamos, que os dados recolhidos a partir de sensores de baixo custo, como sensores de presença e ocupação, podem ser utilizados para fornecer evidências sobre os hábitos diários de mobilidade dos idosos que vivem sozinhos em casa e detetar desta forma mudanças nas suas rotinas. Neste trabalho, validamos esta hipótese, desenvolvendo um sistema que aprende automaticamente as transições diárias entre divisões da habitação e hábitos de estadia em cada uma dessas divisões em cada momento do dia e consequentemente gera alarmes sempre que os desvios são detetados. Apresentamos neste trabalho um algoritmo que processa os fluxos de dados dos diferentes sensores e identifica características que descrevem a rotina diária de mobilidade de um idoso que vive sozinho em casa. Para isso foi definido um conjunto de dimensões baseadas nos dados extraídos dos sensores, como parte do nosso Behaviour Monitoring System (BMS). Fomos capazes de detetar com um atraso mínimo os comportamentos incomuns e ao mesmo tempo, durações de confirmação da deteção elevadas, de tal modo suficientes para um conjunto comum de situações anormais. Apresentamos e avaliamos o BMS com dados sintetizados, produzidos por um gerador de dados desenvolvido para este efeito e projetado para simular diferentes perfis de mobilidade de indivíduos em casa, e também com dados reais obtidos de trabalhos de investigação anteriores. Os resultados indicam que o BMS deteta várias mudanças de mobilidade que podem ser sintomas para problemas de saúde comuns. O sistema proposto é uma abordagem útil para a aprendizagem dos hábitos de mobilidade em ambientes domésticos, com potencial para detetar alterações comportamentais que ocorrem devido a problemas de saúde, e assim encorajar a monitorização dos comportamentos e dos cuidados de saúde dos idosos.Unusual changes in the regular daily mobility routine of an elderly at home can be an indicator or early symptoms for developing a health problem. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and stays habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensor data streams and compute features that describe the daily mobility routine of an elderly living alone at home. This was done by defining a set of sensor-driven dimensions extracted from the sensor data as part of our Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different users’ mobility profiles at home, and also with real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at home environments, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder’s care

The use of ‘off-the-shelf’ GPS technology to support people living with dementia and their care partners

This doctoral thesis explores the perceptions and experiences of people living with dementia and their care partners on off-the-shelf Global Positioning System (GPS) technology. ‘Off-the-shelf’ GPS devices are readily available, without a specific use attached and are often more economical than products labelled for use within dementia care.This study was designed to guarantee that people living with dementia were an essential part of the study. This was not research ‘about them’, or through the use of a proxy to explore what people with dementia might do instead, this research sought to ensure that their voice was heard, understood, and had an impact. This was done through a two-phased approach. The first phase acted as a consultation session, employing a focus group to explore the views, opinions, and experiences of participants living with dementia and care partners on a range of off-the-shelf GPS devices. Findings indicated that the wearability, usability, and cost of products were integral to their success and adoption. Phase two utilised these findings with eighteen new participants using the off-the-shelf device chosen by participants of phase one, alongside training and technical support, for a period of three months. In-depth interviews with participants took place before and after this period of use.Findings resonate with existing research, but also build upon the evidence base, to show the benefits and challenges of using this technology. This study demonstrates the benefits of GPS in relation to a person’s psycho-social needs, with barriers found relating to a person’s relationship with technology. The approach taken provided new insights into conducting research with people living with dementia as well as during times of crisis. Conclusions reached argue that off-the-shelf GPS devices are a viable, inclusive alternative to ‘dementia branded’ products, that could reduce healthcare inequalities and increase access to potentially vital technology