Embedding a Grid of Load Cells into a Dining Table for Automatic Monitoring and Detection of Eating Events

This dissertation describes a “smart dining table” that can detect and measure consumption events. This work is motivated by the growing problem of obesity, which is a global problem and an epidemic in the United States and Europe. Chapter 1 gives a background on the economic burden of obesity and its comorbidities. For the assessment of obesity, we briefly describe the classic dietary assessment tools and discuss their drawback and the necessity of using more objective, accurate, low-cost, and in-situ automatic dietary assessment tools. We explain in short various technologies used for automatic dietary assessment such as acoustic-, motion-, or image-based systems. This is followed by a literature review of prior works related to the detection of weights and locations of objects sitting on a table surface. Finally, we state the novelty of this work. In chapter 2, we describe the construction of a table that uses an embedded grid of load cells to sense the weights and positions of objects. The main challenge is aligning the tops of adjacent load cells to within a few micrometer tolerance, which we accomplish using a novel inversion process during construction. Experimental tests found that object weights distributed across 4 to 16 load cells could be measured with 99.97±0.1% accuracy. Testing the surface for flatness at 58 points showed that we achieved approximately 4.2±0.5 um deviation among adjacent 2x2 grid of tiles. Through empirical measurements we determined that the table has a 40.2 signal-to-noise ratio when detecting the smallest expected intake amount (0.5 g) from a normal meal (approximate total weight is 560 g), indicating that a tiny amount of intake can be detected well above the noise level of the sensors. In chapter 3, we describe a pilot experiment that tests the capability of the table to monitor eating. Eleven human subjects were video recorded for ground truth while eating a meal on the table using a plate, bowl, and cup. To detect consumption events, we describe an algorithm that analyzes the grid of weight measurements in the format of an image. The algorithm segments the image into multiple objects, tracks them over time, and uses a set of rules to detect and measure individual bites of food and drinks of liquid. On average, each meal consisted of 62 consumption events. Event detection accuracy was very high, with an F1-score per subject of 0.91 to 1.0, and an F1 score per container of 0.97 for the plate and bowl, and 0.99 for the cup. The experiment demonstrates that our device is capable of detecting and measuring individual consumption events during a meal. Chapter 4 compares the capability of our new tool to monitor eating against previous works that have also monitored table surfaces. We completed a literature search and identified the three state-of-the-art methods to be used for comparison. The main limitation of all previous methods is that they used only one load cell for monitoring, so only the total surface weight can be analyzed. To simulate their operations, the weights of our grid of load cells were summed up to use the 2D data as 1D. Data were prepared according to the requirements of each method. Four metrics were used to evaluate the comparison: precision, recall, accuracy, and F1-score. Our method scored the highest in recall, accuracy, and F1-score; compared to all other methods, our method scored 13-21% higher for recall, 8-28% higher for accuracy, and 10-18% higher for F1-score. For precision, our method scored 97% that is just 1% lower than the highest precision, which was 98%. In summary, this dissertation describes novel hardware, a pilot experiment, and a comparison against current state-of-the-art tools. We also believe our methods could be used to build a similar surface for other applications besides monitoring consumption

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Ubiquitous computing and natural interfaces for environmental information

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe next computing revolution‘s objective is to embed every street, building, room and object with computational power. Ubiquitous computing (ubicomp) will allow every object to receive and transmit information, sense its surroundings and act accordingly, be located from anywhere in the world, connect every person. Everyone will have the possibility to access information, despite their age, computer knowledge, literacy or physical impairment. It will impact the world in a profound way, empowering mankind, improving the environment, but will also create new challenges that our society, economy, health and global environment will have to overcome. Negative impacts have to be identified and dealt with in advance. Despite these concerns, environmental studies have been mostly absent from discussions on the new paradigm. This thesis seeks to examine ubiquitous computing, its technological emergence, raise awareness towards future impacts and explore the design of new interfaces and rich interaction modes. Environmental information is approached as an area which may greatly benefit from ubicomp as a way to gather, treat and disseminate it, simultaneously complying with the Aarhus convention. In an educational context, new media are poised to revolutionize the way we perceive, learn and interact with environmental information. cUbiq is presented as a natural interface to access that information

Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective

Les tecnologies de la informació i la comunicació han canviat les nostres vides de manera irreversible. La indústria sanitària, una de les indústries més grans i de major creixement, està dedicant molts esforços per adoptar les últimes tecnologies en la pràctica mèdica diària. Per tant, no és sorprenent que els paradigmes sanitaris estiguin en constant evolució cercant serveis més eficients, eficaços i sostenibles. En aquest context, el potencial de la computació ubiqua mitjançant telèfons intel·ligents, rellotges intel·ligents i altres dispositius IoT ha esdevingut fonamental per recopilar grans volums de dades, especialment relacionats amb l'estat de salut i la ubicació de les persones. Les millores en les capacitats de detecció juntament amb l'aparició de xarxes de telecomunicacions d'alta velocitat han facilitat la implementació d'entorns sensibles al context, com les cases i les ciutats intel·ligents, capaços d'adaptar-se a les necessitats dels ciutadans. La interacció entre la computació ubiqua i els entorns sensibles al context va obrir la porta al paradigma de la salut intel·ligent, centrat en la prestació de serveis de salut personalitzats i de valor afegit mitjançant l'explotació de grans quantitats de dades sanitàries, de mobilitat i contextuals. No obstant, la gestió de dades sanitàries, des de la seva recollida fins a la seva anàlisi, planteja una sèrie de problemes desafiants a causa del seu caràcter altament confidencial. Aquesta tesi té per objectiu abordar diversos reptes de seguretat i privadesa dins del paradigma de la salut intel·ligent. Els resultats d'aquesta tesi pretenen ajudar a la comunitat científica a millorar la seguretat dels entorns intel·ligents del futur, així com la privadesa dels ciutadans respecte a les seves dades personals i sanitàries.Las tecnologías de la información y la comunicación han cambiado nuestras vidas de forma irreversible. La industria sanitaria, una de las industrias más grandes y de mayor crecimiento, está dedicando muchos esfuerzos por adoptar las últimas tecnologías en la práctica médica diaria. Por tanto, no es sorprendente que los paradigmas sanitarios estén en constante evolución en busca de servicios más eficientes, eficaces y sostenibles. En este contexto, el potencial de la computación ubicua mediante teléfonos inteligentes, relojes inteligentes, dispositivos wearables y otros dispositivos IoT ha sido fundamental para recopilar grandes volúmenes de datos, especialmente relacionados con el estado de salud y la localización de las personas. Las mejoras en las capacidades de detección junto con la aparición de redes de telecomunicaciones de alta velocidad han facilitado la implementación de entornos sensibles al contexto, como las casas y las ciudades inteligentes, capaces de adaptarse a las necesidades de los ciudadanos. La interacción entre la computación ubicua y los entornos sensibles al contexto abrió la puerta al paradigma de la salud inteligente, centrado en la prestación de servicios de salud personalizados y de valor añadido mediante la explotación significativa de grandes cantidades de datos sanitarios, de movilidad y contextuales. No obstante, la gestión de datos sanitarios, desde su recogida hasta su análisis, plantea una serie de cuestiones desafiantes debido a su naturaleza altamente confidencial. Esta tesis tiene por objetivo abordar varios retos de seguridad y privacidad dentro del paradigma de la salud inteligente. Los resultados de esta tesis pretenden ayudar a la comunidad científica a mejorar la seguridad de los entornos inteligentes del futuro, así como la privacidad de los ciudadanos con respecto a sus datos personales y sanitarios.Information and communication technologies have irreversibly changed our lives. The healthcare industry, one of the world’s largest and fastest-growing industries, is dedicating many efforts in adopting the latest technologies into daily medical practice. It is not therefore surprising that healthcare paradigms are constantly evolving seeking for more efficient, effective and sustainable services. In this context, the potential of ubiquitous computing through smartphones, smartwatches, wearables and IoT devices has become fundamental to collect large volumes of data, including people's health status and people’s location. The enhanced sensing capabilities together with the emergence of high-speed telecommunication networks have facilitated the implementation of context-aware environments, such as smart homes and smart cities, able to adapt themselves to the citizens needs. The interplay between ubiquitous computing and context-aware environments opened the door to the so-called smart health paradigm, focused on the provision of added-value personalised health services by meaningfully exploiting vast amounts of health, mobility and contextual data. However, the management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation addresses several security and privacy challenges within the smart health paradigm. The results of this dissertation are intended to help the research community to enhance the security of the intelligent environments of the future as well as the privacy of the citizens regarding their personal and health data

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

Quiet Supersonic Flights 2018 (QSF18) Test: Galveston, Texas Risk Reduction for Future Community Testing with a Low-Boom Flight Demonstration Vehicle

The Quiet Supersonic Flights 2018 (QSF18) Program was designed to develop tools and methods for demonstration of overland supersonic flight with an acceptable sonic boom, and collect a large dataset of responses from a representative sample of the population. Phase 1 provided the basis for a low amplitude sonic boom testing in six different climate regions that will enable international regulatory agencies to draft a noise-based standard for certifying civilian supersonic overland flight. Phase 2 successfully executed a large scale test in Galveston, Texas, developed well documented data sets, calculated dose response relationships, yielded lessons, and identified future risk reduction activities

Advanced space system concepts and their orbital support needs (1980 - 2000). Volume 2: Final report

The results are presented of a study which identifies over 100 new and highly capable space systems for the 1980-2000 time period: civilian systems which could bring benefits to large numbers of average citizens in everyday life, much enhance the kinds and levels of public services, increase the economic motivation for industrial investment in space, expand scientific horizons; and, in the military area, systems which could materially alter current concepts of tactical and strategic engagements. The requirements for space transportation, orbital support, and technology for these systems are derived, and those requirements likely to be shared between NASA and the DoD in the time period identified. The high leverage technologies for the time period are identified as very large microwave antennas and optics, high energy power subsystems, high precision and high power lasers, microelectronic circuit complexes and data processors, mosaic solid state sensing devices, and long-life cryogenic refrigerators

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living