Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación y Universidades

Digitalization Capacity for Knowledge Acquisition: Learning from Health Monitoring

Digital transformation boosts the integration of intelligent data into all areas of society, from personal streams of life-span, an organization business workflows, to the whole ecosystem of different industries. The continuous connectivity and interaction in the digital world pave the way to learn knowledge from the variety of massive data. The Internet of Things (IoT) is a promising practice in the digitalization process. Its basic spirit is to thrust a paradigm that everything (machine and people) can be seamlessly connected into an IoT network by sensors. Toward the next frontier society 5.0, it is aimed for a prosperous human-centered society where people can have a high quality of life. However, general IoT architectures and data value chain models are still device/platform-specific, which lacks necessary emphasis on people dimension. Health as the core aspect of people has a significant impact on their quality of life. The adverse health factors may cross the entire lifespan: from home to workplace, from commute to work or fitness, and even elderly people care homes. To center the research work, the research question was pinpointed: How to accelerate and enhance people’s health and well-being in the IoT data value chain? On the premise of research status-quo and the pinpointed research question, four specific research objectives were defined as follows. 1. Enhance people dimension, especially health and well-being perspective in conventional IoT architectures. 2. Develop continuous long term monitoring solutions to support better health management. 3. Learn psychological and physical health impact from (e.g., workers) a group of people over the activities. 4. Accelerate healthrelated data sharing with security trust and privacy assurance. To fulfill the research objectives, the action research method was employed as a theoretical approach to analyze and implement the research; a guiding framework was designed to set up the research theme and context, the applied key concepts, enabling technologies, devices, and developed prototypes were introduced under the guiding framework; furthermore, several study designs were detailed presented including experimental setup, data collection, and relevant data analysis method. Toward a human-centered society, the future of IoT is also seeking enhanced people-centered solutions. To accelerate and facilitate health-related data-driven knowledge acquisition and data value chain to society, organizations, and individuals, the research leverages advance technologies such as IoT, Smart Wearables, DLT, and machine learning techniques. To summarize, the study focuses on the health dimension, the thesis generalizes highly the main contributions. 1. The study proposed IoT application architectures such as Healthy operator 4.0 architecture and proved their feasibility through real-world application cases in the industry. 2. Three longterm monitoring solutions were developed using low-cost IoT devices and successfully adopted in practical usage to continuously collect health-related parameters. 3. Different data mining approaches and machine learning methods were investigated and compared to learn health impacts over the activities from a group of people. The method chosen was proved to be capable of better understanding people’s behavioral patterns and hidden rules, by the real-world empirical analysis conducted in both Spain and the USA. 4. A data sharing solution was designed in the study, that integrates DLT (IOTA Tangle) to IoT data management, by which data transparency and data ownership can be implemented under a secure, fee-less, and trust data sharing mechanism. The value produced by the contributions is reflected on the individual level, organization level, and society level, which lies in societal aspects such as smart environment, industry 4.0, and smart city. With data-driven AI technology booming, big data analytic era comes. The future of work is now. The advance technology such as deep learning, Hadoop, Kubernetes, and Spark can be employed to dig knowledge out of data. The IoT big data analytic can achieve an improved understanding of data for individuals, organizations, and society, to make efficient and effective decisions. ----------RESUMEN---------- La transformación digital acelera la integración de la inteligencia de datos en todas las áreas de la sociedad, desde los flujos de datos personales relativos a las actividades diarias, pasando por los flujos de trabajo de procesos de negocio en las organizaciones, hasta alcanzar todo el ecosistema de relaciones inter-industrias. La conectividad universal continua y la interacción en el universo digital facilita la obtención de conocimiento desde una gran variedad de datos masivos. El internet de las cosas (IoT) es una práctica prometedora in la digitalización de procesos. Su espíritu primario es potenciar un paradigma en el que tanto las máquinas como la gente pueden ser conectados de un modo integrado en las redes IoT de sensores. Manteniendo el foco en la siguiente frontera, "sociedad 5.0", se busca desarrollar una sociedad que potencie la dimensión humana, donde la gente podrá desarrollar una elevada calidad de vida. No obstante, tanto la arquitectura general del IoT como los flujos de valor de los datos obtenidos son aún específicos para los dispositivos o plataformas, lo que dificulta poner el énfasis en la dimensión humana general. La salud, como un aspecto central de interés para las personas tiene un impacto muy significativo en su calidad de vida. Factores negativos en relación a la salud pueden ser persistentes en el tiempo, tanto en ámbitos domésticos como laborales, y pueden ocurrir tanto en el transporte diario como en actividades deportivas o en el cuidado de personas de edad avanzada. Para centrar el trabajo de la tesis la pregunta de investigación ha sido resaltada: ¿Cómo acelerar y potenciar la dimensión salud y bienestar en la población a través de los flujos de valor correspondientes a esos datos? Sobre las premisas definidas pro el status-quo de investigación y por la pregunta de investigación cuatro objetivos específicos de investigación han sido definidos: 1.-Potenciar la dimensión humana, en particular salud y bienestar en las arquitecturas basadas en IoT. 2.- Desarrollar soluciones de monitorización de parámetros a largo plazo, que permitan una mejor gestión de los factores con influencia en la salud. 3.- Comprender el impacto en la salud tanto física como psicológica en grupos de población (por ejemplo trabajadores) durante el desempeño de su actividad. 4.- Acelerar el uso compartido de los datos relativos a la salud, cumpliendo criterios de confianza y privacidad suficientes. Para alcanzar los objetivos de investigación el método de investigación activa ha sido empleado como aproximación teórica para analizar e implementar la investigación. Se ha desarrollado un marco de referencia que guíe el proceso y permita establecer el tema de investigación y su contexto, los conceptos clave, las tecnologías habilitantes, los dispositivos, así como desarrollar los prototipos de acuerdo al marco desarrollado. Adicionalmente, varios estudios de detalle han sido desarrollados, incluyendo su ciclo de vida de configuración, captura de datos y análisis de los mismos. El futuro del IoT enfocado a una sociedad centrada en la dimensión humana busca también desarrollar soluciones centradas en la gente. Para acelerar y facilitar la generación y adquisición de conocimiento proveniente de los flujos de datos que pueda beneficiar a la sociedad (organizaciones e individuos), la investigación balancea tecnologías avanzadas, tales como IoT, dispositivos inteligentes, Libros Mayores Distribuidos y técnicas de aprendizaje automático. El estudio se centra en la dimensión de salud y de modo resumido sus principales contribuciones son: 1.- Proponer aplicaciones de arquitecturas IoT como Operario Saludable 4.0, demostrando su factibilidad a través de aplicaciones con casos de uso industriales. 2.- Desarrollar tres soluciones de monitorización de largo plazo usando dispositivos IoT de bajo coste con una implementación satisfactoria en las aplicaciones reales que permite la recogida de datos relativos a parámetros relacionados con salud. 3.- Investigar diferentes aproximaciones de minado de datos y métodos de aprendizaje automático, así como comparar sus resultados para comprender mejor las implicaciones sanitarias de comportamientos cuando se consideran agrupados. La selección del método elegido ha mostrado su utilidad en explicar los patrones de comportamiento de la gente y determinadas reglas no explícitas, al observar comportamientos de grupos de personas tanto en España como en E.E.U.U. 4.- Diseñar una solución de compartición de datos que integra la tecnología DLT (Tangle de IoTA) para facilitar el manejo de datos de IoT, de modo que transparencia y propiedad de los mismos estén aseguradas a través de mecanismos confiables y sin coste de transacción. El valor creado a través de las contribuciones de esta tesis se reflejan tanto a nivel individual como de sociedad en general, vinculado a gestión ambiental inteligente, industria 4.0 y ciudades inteligentes. Con las aplicaciones de inteligencia artificial aprovechando los flujos de datos en plena explosión, la era de creación de valor usando la analítica de datos masiva está en sus comienzos. Tecnologías avanzadas como el aprendizaje profundo, Hadoop, Kubernetes y Spark pueden ser empleadas para destilar conocimiento desde los datos. La analítica de datos masiva a partir de IoT se espera que contribuya a transformar el conocimiento tanto para individuos, como organizaciones y sociedad en general, facilitando tomas de decisiones más eficientes y efectivas

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology

Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architectur

Next Frontier for Health Systems: Learning from Patient’s Behavior and Fuzzy Factors Identification

International audienceThis paper aims to explore challenges and opportunities that technology brings when applied to understand effects that patient’s behaviour has in relationship with evolution of the disease. The paper will review the contributions from the Internet of Things (IoT) paradigm, but also it will consider alternate sources to collect feelings and interests which are related to the big-data dimension, like social media exploration, etc. Based on the current status of the technology some managerial considerations are discussed as well. Two questions are addressed: The first one highlights technological limitations from the current and fragmented approach, including concerns related to privacy and ownership. In response, the paper proposes a holistic framework helping in easing the IoT use by proposing integrated solutions to several of the identified concerns. The second one is related to the patient’s motivation for adopting such IoT solutions. Some future researches is identified to explore the impact of "easy to use" technological solutions on patients motivation in sustainable adoption

The Use of the Internet of Things for Estimating Personal Pollution Exposure

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases

Healthy Operator 4.0: A Human Cyber-Physical System Architecture for Smart Workplaces

Recent advances in technology have empowered the widespread application of cyber-physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human-cyber-physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits

Tourists traits analysis on social networks

The travel and tourism industry is flourishing worldwide. It is vital for tourism-related supplier and markers to understand tourist traits in order to target tourism consumers and further assist decision makings. With the development of social networks, tourists published large quantities of travel experiences on social networks where they disclose their traits explicitly and implicitly. In this paper, we design a methodology for tourism traits analysis based on social networks, which includes three components: tourist demographic analysis, tourist social influences analysis, and tourist behavior analysis. Sina Weibo based Chinese tourists in Switzerland analysis is as a case study for our methodology, and different findings are obtained. Those findings are beneficial to tourism-related suppliers and markers to make valuable strategies. And our proposed methodology could be applied to analyze tourist traits in any social network platform