Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces

Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.This work has been supported by the Cost Action TU1306, called CYBERPARKS: Fostering knowledge about the relationship between Information and Communication Technologies and Public Spaces supported by strategies to improve their use and attractiveness, the Spanish Ministry of Economy and Competitiveness under the ESPHIA project (ref. TIN2014-56042-JIN) and the TARSIUS project (ref. TIN2015-71564-C4-4-R), and the Basque Country Department of Education under the BLUE project (ref. PI-2016-0010). The authors would also like to thank the staff of UbiSive s.r.l. for the support in developing the application

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

APPLICATIONS OF MACHINE LEARNING AND COMPUTER VISION FOR SMART INFRASTRUCTURE MANAGEMENT IN CIVIL ENGINEERING

Machine Learning and Computer Vision are the two technologies that have innovative applications in diverse fields, including engineering, medicines, agriculture, astronomy, sports, education etc. The idea of enabling machines to make human like decisions is not a recent one. It dates to the early 1900s when analogies were drawn out between neurons in a human brain and capability of a machine to function like humans. However, major advances in the specifics of this theory were not until 1950s when the first experiments were conducted to determine if machines can support artificial intelligence. As computation powers increased, in the form of parallel computing and GPU computing, the time required for training the algorithms decreased significantly. Machine Learning is now used in almost every day to day activities. This research demonstrates the use of machine learning and computer vision for smart infrastructure management. This research’s contribution includes two case studies – a) Occupancy detection using vibration sensors and machine learning and b) Traffic detection, tracking, classification and counting on Memorial Bridge in Portsmouth, NH using computer vision and machine learning. Each case study, includes controlled experiments with a verification data set. Both the studies yielded results that validated the approach of using machine learning and computer vision. Both case studies present a scenario where in machine learning is applied to a civil engineering challenge to create a more objective basis for decision-making. This work also includes a summary of the current state-of-the -practice of machine learning in Civil Engineering and the suggested steps to advance its application in civil engineering based on this research in order to use the technology more effectively

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

ICT tools for data management and analysis to support decisional process oriented to sustainable agri-food chains

Il settore agroalimentare sta affrontando delle sfide globali. La prima riguarda sfamare la popolazione mondiale che nel 2050, secondo le proiezioni delle Nazioni Unite, raggiungerà quota 9,3 miliardi di persone. La seconda sfida riguarda la richiesta da parte dei consumatori di prodotti ottenuti da filiere agroalimentari sempre più sostenibili, sicure e trasparenti. In particolare, l’Agricoltura sostenibile è una tecnica di gestione in grado di preservare la diversità biologica, la produttività, la capacità di rigenerazione, la vitalità e l’abilità alla funzione di un ecosistema agricolo, assicurandone, oggi e in futuro, le funzioni ecologiche, economiche e sociali a livello locale, nazionale ed globale, senza danneggiare altri ecosistemi. Quindi, per fronteggiare la sfida dell’agricoltura sostenibile, gli agricoltori devono aumentare la qualità e la quantità della produzione, riducendo l’impatto ambientale attraverso nuovi strumenti e nuove strategie di gestione. Questo lavoro analizza l’integrazione nel settore agroalimentare di alcune tecnologie e metodologie ICT per l’acquisizione, gestione e analisi dei dati, come la tecnologia RFID (Radio Frequency IDentification), i FMIS (Farm Management Information Systems), i DW (Data Warehouse) e l’approccio OLAP (On-Line Analytical Processing). Infine, l’adozione delle tecnologie ICT da parte di vere aziende è stata valutata attraverso un questionario. Al riguardo dell’adozione delle tecnologie RFID, questo lavoro analizza l’opportunità di trasferimento tecnologico relativo al monitoraggio e controllo dei prodotti agroalimentari tramite l’utilizzo di sensori innovativi, intelligenti e miniaturizzati. Le informazioni riguardanti lo stato del prodotto sono trasferite in tempo reale in wireless, come previsto dalla tecnologia RFID. In particolare, due soluzioni RFID sono state analizzate, evidenziando vantaggi e punti critici in confronto ai classici sistemi per assicurare la tracciabilità e la qualità dei prodotti agroalimentari. Quindi, questo lavoro analizza la possibilità di sviluppare una struttura che combina le tecnologie della Business Intelligence con i principi della Protezione Integrata (IPM) per aiutare gli agricoltori nel processo decisionale, andando a diminuire l’impatto ambientale ed aumentare la performance produttiva. L’IPM richiede di utilizzare simultaneamente diverse tecniche di protezione delle colture per il controllo dei parassiti e patogeni tramite un approccio ecologico ed economico. Il sistema di BI proposto è chiamato BI4IPM e combina l’approccio OLTP (On-Line Transaction Processing) con quello OLAP per verificare il rispetto dei disciplinari di produzione integrata. BI4IPM è stato testato con dati provenienti da vere aziende olivicole pugliesi. L’olivo è una delle principali colture a livello globale e la Puglia è la prima regione produttrice in Italia, con un gran numero di aziende che generano dati sull’IPM. Le strategie di protezione delle colture sono correlate alle condizioni climatiche, considerando la forte relazione tra clima, colture e parassiti. Quindi, in questo lavoro è presentato un nuovo e avanzato modello OLAP che integra il GSI (Growing Season Index), un modello fenologico, per comparare indirettamente le aziende agricole dal punto di vista climatico. Il sistema proposto permette di analizzare dati IPM di diverse aziende agricole che presentano le stesse condizioni fenologiche in un anno al fine di individuare best practices e di evidenziare e spiegare pratiche differenti adottate da aziende che lavorano in differenti condizioni climatiche. Infine, è stata effettuata un’indagine al fine di capire come le aziende agricole della Basilicata si raggruppano in funzione del livello di innovazione adottato. È stato utilizzato un questionario per domandare alle aziende se adottano strumenti ICT, ed eventualmente in quale processo produttivo o di management vengano usati. È stata quindi effettuata un’analisi cluster sui dati raccolti. I risultati mostrano che, usando il metodo di clustering k-means, appaiono due gruppi: gli innovatori e gli altri. Mentre, applicando la rappresentazione boxlot, si ottengono 3 gruppi: innovatori, utilizzatori precoci e ritardatari.The Agri-Food sector is facing global challenges. The first issue concerns feeding a world population that in 2050, according to United Nations projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food chains. In particular, the Sustainable agriculture is a management strategy able to preserve the biological diversity, productivity, regeneration capacity, vitality and ability to function of an agricultural ecosystem, ensuring, today and in the future, significant ecological, economic and social functions at the local, national and global scales, without harming other ecosystems. Therefore, to face the challenge of the sustainable agriculture, farmers need to increase quality and quantity of the production, reducing the environmental impact through new management strategies and tools. This work explores the integration of several ICT technologies and methodologies in the agri-food sector for the data acquisition, management and analysis, such as RFID technology, Farm Management Information Systems (FMIS), Data Warehouse (DW) and On-Line Analytical Processing (OLAP). Finally, the adoption of the ICT technologies by real farms is evaluated through a survey. Regarding the adoption of the RFID technology, this work explores an opportunity for technology transfer related to the monitoring and control of agri-food products, based on the use of miniaturized, smart and innovative sensors. The information concerning to the state of the product is transferred in real time in a wireless way, according to the RFID technology. In particular, two technical solutions involving RFID are provided, highlighting the advantages and critical points referred to the normal system used to ensure the traceability and the quality of the agri-food products. Therefore, this work explores the possibility of developing a framework that combines business intelligence (BI) technologies with Integrated Pest Management (IPM) principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with OLAP to verify adherence to the IPM technical specifications. BI4IPM is tested with data from real Apulian olive crop farms. Olive tree is one of the most important crop at global scale and Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate IPM data. The crop protection strategies are correlated to the climate conditions considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced OLAP model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions. Finally, a survey aimed at investigating how Lucania' farms cluster according to the level of innovation adopted was performed. It was used a questionnaire for asking if farms adopt ICTs tools and, in case, what type they involved in managing and/or production processes. It has been done a cluster analysis on collected data. Results show that, using k-means clustering method, appear two clusters: innovators, remaining groups. While, using boxplot representation, clustered three groups: innovators, early adopters and laggards

JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems

This book is about JIDOKA, a Japanese management technique coined by Toyota that consists of imbuing machines with human intelligence. The purpose of this compilation of research articles is to show industrial leaders innovative cases of digitization of value creation processes that have allowed them to improve their performance in a sustainable way. This book shows several applications of JIDOKA in the quest towards an integration of human and AI within Industry 4.0 Cyber Physical Manufacturing Systems. From the use of artificial intelligence to advanced mathematical models or quantum computing, all paths are valid to advance in the process of human–machine integration

Socially assistive robots adoption

Mestrado em Gestão de Sistemas de InformaçãoCom o rápido envelhecimento da população mundial verifica-se um aumento do número de pessoas a requerer cuidados da parte de outrem. Contudo, o fenómeno mencionado traz, também, uma redução do número de pessoas disponíveis para prestar tais cuidados. Como tal, as sociedades são cada vez mais forçadas a lidar com esta discrepância que deve ser colmatada de modo a permitir aos idosos envelhecer com a dignidade que estes merecem. Uma forma de o fazer pode ser encontrada no campo das tecnologias de apoio, nomeadamente na figura dos Socially Assistive Robots (SAR): robots que apoiam os humanos através da interação social. O objetivo do presente estudo é investigar a propensão da população idosa portuguesa para a adoção destes robots. Para esse fim, primeiro, foi levada a cabo uma revisão da literatura onde foi recolhida informação relativa às formas em que os SAR podem apoiar os idosos, fatores que afetam a adoção destes robots e as principais preocupações que se afiguram num processo de adoção. De seguida, um estudo quantitativo baseado na aplicação de questionários à população portuguesa com 65 ou mais anos foi conduzido. De modo a estudar os fatores que influenciam a adoção dos robots, uma adaptação da segunda versão do modelo Unified Theory of Acceptance and Use of Technology foi criada e operacionalizada. Os resultados do estudo, não sendo tão abrangentes como inicialmente desejado, servem para complementar a base de conhecimento a ser constituída em torno deste tópico, iluminando e informando o trabalho daqueles que desenvolvem SAR.With the world population ageing rapidly comes an increase in the number of people requiring care from others. However, this phenomenon also brings a decrease in the number of people capable of providing such care. Societies are then increasingly left with a gap which must be bridged for the elderly to be afforded to age with the dignity which they deserve. One way to close said gap can be found in the field of assistive technologies, namely in the form of Socially Assistive Robots (SAR): robots designed to aid humans through social interaction. The aim of the current study is to investigate the propension of elderly Portuguese people for SAR adoption. Thus, firstly a review of literature focused on the ways in which SAR can aid the elderly, robot acceptance factors and common adoption concerns was put together. Secondly, a quantitative study based on the application of questionnaires to the Portuguese population aged 65 and over was conducted. In order to study the robot acceptance factors, an adaptation of the second version of Unified Theory of Acceptance and Use of Technology was created and deployed. The study results, while not as encompassing as initially desired, serve to complement the knowledge base being currently built around this topic, illuminating and informing the work of individuals focused on designing Socially Assistive Robots.info:eu-repo/semantics/publishedVersio

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario