73 research outputs found

    Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

    Get PDF
     With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail

    Contextual and design factors that influence the use of consumer technologies for self-management of stress by teachers

    Get PDF
    Persistent psychosocial stress is endemic in the modern workplace, including amongst secondary school teachers in England. There is intense interest in the potential role of digital technology such as apps, wearables and online programmes to support stress management but insufficient understanding of how the contexts of teachers’ work influence their use. Using a constructivist paradigm, a series of qualitative studies was conducted to understand the influence of these contextual factors. First semi-structured qualitative interviews with teachers were thematically analysed to reveal the physical, social and cultural contextual constraints on teachers’ stress management. Then to enable teachers’ choice of consumer technology for the longitudinal study, an analytical study generated a populated taxonomy of self-management strategies for stress with digital support options. This was presented in workshops to enable some informed choice. Finally, the qualitative longitudinal summer term study explored eight teachers’ experiences of using their chosen technology in their daily lives. The pandemic meant interviews were online and teachers were mainly working from home. The study was extended with six participants into the autumn term when all teachers had returned to school premises. Cross-case analysis revealed the teachers’ experiences of using technology for stress management included the explanatory power of contextually mediated data, generating awareness, permission to self-care and empathy. The findings suggest implications for self-determination theory (SDT). Thematic analysis revealed facilitators and barriers to using the technology in the school context. There are associated implications for school wellbeing support and designers, and considerations for the Unified Theory of Acceptance and Use of Technology (UTAUT). This thesis’ main contributions include unique insight into teachers’ experiences of consumer technologies for workplace stress management and the technology features that facilitate self-care. Stress awareness derived from interaction with the technology and personal data gave teachers permission to self-care. Facilitators included brief, discreet interactions and contextually relevant prompts and information. Barriers to use included insufficient technology instructions, and contextual constraints of the relentless work culture, social stigma and lack of privacy. This thesis also documents an innovative process for developing and populating a taxonomy to facilitate technology selection, including specifically for teachers managing stress. Finally, it makes recommendations of interest to designers, school leaders and policy makers seeking to improve teachers’ ability to digitally support their stress self-management

    Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications

    Get PDF
    Nanocellulose already proved to be a highly relevant material for biomedical applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive research, a notable number of emerging applications are still being developed. Interestingly, this drive is not solely based on the nanocellulose features, but also heavily dependent on sustainability. The three core nanocelluloses encompass cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their near future applicability. By analyzing the pristine core nanocellulose, none display cytotoxicity. However, CNF has the highest potential to fail long-term biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being elite materials in the urgent replacement of our petrochemical dependence

    Earables: Wearable Computing on the Ears

    Get PDF
    Kopfhörer haben sich bei Verbrauchern durchgesetzt, da sie private AudiokanĂ€le anbieten, zum Beispiel zum Hören von Musik, zum Anschauen der neuesten Filme wĂ€hrend dem Pendeln oder zum freihĂ€ndigen Telefonieren. Dank diesem eindeutigen primĂ€ren Einsatzzweck haben sich Kopfhörer im Vergleich zu anderen Wearables, wie zum Beispiel Smartglasses, bereits stĂ€rker durchgesetzt. In den letzten Jahren hat sich eine neue Klasse von Wearables herausgebildet, die als "Earables" bezeichnet werden. Diese GerĂ€te sind so konzipiert, dass sie in oder um die Ohren getragen werden können. Sie enthalten verschiedene Sensoren, um die FunktionalitĂ€t von Kopfhörern zu erweitern. Die rĂ€umliche NĂ€he von Earables zu wichtigen anatomischen Strukturen des menschlichen Körpers bietet eine ausgezeichnete Plattform fĂŒr die Erfassung einer Vielzahl von Eigenschaften, Prozessen und AktivitĂ€ten. Auch wenn im Bereich der Earables-Forschung bereits einige Fortschritte erzielt wurden, wird deren Potenzial aktuell nicht vollstĂ€ndig abgeschöpft. Ziel dieser Dissertation ist es daher, neue Einblicke in die Möglichkeiten von Earables zu geben, indem fortschrittliche SensorikansĂ€tze erforscht werden, welche die Erkennung von bisher unzugĂ€nglichen PhĂ€nomenen ermöglichen. Durch die EinfĂŒhrung von neuartiger Hardware und Algorithmik zielt diese Dissertation darauf ab, die Grenzen des Erreichbaren im Bereich Earables zu verschieben und diese letztlich als vielseitige Sensorplattform zur Erweiterung menschlicher FĂ€higkeiten zu etablieren. Um eine fundierte Grundlage fĂŒr die Dissertation zu schaffen, synthetisiert die vorliegende Arbeit den Stand der Technik im Bereich der ohr-basierten Sensorik und stellt eine einzigartig umfassende Taxonomie auf der Basis von 271 relevanten Publikationen vor. Durch die Verbindung von Low-Level-Sensor-Prinzipien mit Higher-Level-PhĂ€nomenen werden in der Dissertation anschließ-end Arbeiten aus verschiedenen Bereichen zusammengefasst, darunter (i) physiologische Überwachung und Gesundheit, (ii) Bewegung und AktivitĂ€t, (iii) Interaktion und (iv) Authentifizierung und Identifizierung. Diese Dissertation baut auf der bestehenden Forschung im Bereich der physiologischen Überwachung und Gesundheit mit Hilfe von Earables auf und stellt fortschrittliche Algorithmen, statistische Auswertungen und empirische Studien vor, um die Machbarkeit der Messung der Atemfrequenz und der Erkennung von Episoden erhöhter Hustenfrequenz durch den Einsatz von In-Ear-Beschleunigungsmessern und Gyroskopen zu demonstrieren. Diese neuartigen Sensorfunktionen unterstreichen das Potenzial von Earables, einen gesĂŒnderen Lebensstil zu fördern und eine proaktive Gesundheitsversorgung zu ermöglichen. DarĂŒber hinaus wird in dieser Dissertation ein innovativer Eye-Tracking-Ansatz namens "earEOG" vorgestellt, welcher AktivitĂ€tserkennung erleichtern soll. Durch die systematische Auswertung von Elektrodenpotentialen, die um die Ohren herum mittels eines modifizierten Kopfhörers gemessen werden, eröffnet diese Dissertation einen neuen Weg zur Messung der Blickrichtung. Dabei ist das Verfahren weniger aufdringlich und komfortabler als bisherige AnsĂ€tze. DarĂŒber hinaus wird ein Regressionsmodell eingefĂŒhrt, um absolute Änderungen des Blickwinkels auf der Grundlage von earEOG vorherzusagen. Diese Entwicklung eröffnet neue Möglichkeiten fĂŒr Forschung, welche sich nahtlos in das tĂ€gliche Leben integrieren lĂ€sst und tiefere Einblicke in das menschliche Verhalten ermöglicht. Weiterhin zeigt diese Arbeit, wie sich die einzigarte Bauform von Earables mit Sensorik kombinieren lĂ€sst, um neuartige PhĂ€nomene zu erkennen. Um die Interaktionsmöglichkeiten von Earables zu verbessern, wird in dieser Dissertation eine diskrete Eingabetechnik namens "EarRumble" vorgestellt, die auf der freiwilligen Kontrolle des Tensor Tympani Muskels im Mittelohr beruht. Die Dissertation bietet Einblicke in die Verbreitung, die Benutzerfreundlichkeit und den Komfort von EarRumble, zusammen mit praktischen Anwendungen in zwei realen Szenarien. Der EarRumble-Ansatz erweitert das Ohr von einem rein rezeptiven Organ zu einem Organ, das nicht nur Signale empfangen, sondern auch Ausgangssignale erzeugen kann. Im Wesentlichen wird das Ohr als zusĂ€tzliches interaktives Medium eingesetzt, welches eine freihĂ€ndige und augenfreie Kommunikation zwischen Mensch und Maschine ermöglicht. EarRumble stellt eine Interaktionstechnik vor, die von den Nutzern als "magisch und fast telepathisch" beschrieben wird, und zeigt ein erhebliches ungenutztes Potenzial im Bereich der Earables auf. Aufbauend auf den vorhergehenden Ergebnissen der verschiedenen Anwendungsbereiche und Forschungserkenntnisse mĂŒndet die Dissertation in einer offenen Hard- und Software-Plattform fĂŒr Earables namens "OpenEarable". OpenEarable umfasst eine Reihe fortschrittlicher Sensorfunktionen, die fĂŒr verschiedene ohrbasierte Forschungsanwendungen geeignet sind, und ist gleichzeitig einfach herzustellen. Hierdurch werden die EinstiegshĂŒrden in die ohrbasierte Sensorforschung gesenkt und OpenEarable trĂ€gt somit dazu bei, das gesamte Potenzial von Earables auszuschöpfen. DarĂŒber hinaus trĂ€gt die Dissertation grundlegenden Designrichtlinien und Referenzarchitekturen fĂŒr Earables bei. Durch diese Forschung schließt die Dissertation die LĂŒcke zwischen der Grundlagenforschung zu ohrbasierten Sensoren und deren praktischem Einsatz in realen Szenarien. Zusammenfassend liefert die Dissertation neue Nutzungsszenarien, Algorithmen, Hardware-Prototypen, statistische Auswertungen, empirische Studien und Designrichtlinien, um das Feld des Earable Computing voranzutreiben. DarĂŒber hinaus erweitert diese Dissertation den traditionellen Anwendungsbereich von Kopfhörern, indem sie die auf Audio fokussierten GerĂ€te zu einer Plattform erweitert, welche eine Vielzahl fortschrittlicher SensorfĂ€higkeiten bietet, um Eigenschaften, Prozesse und AktivitĂ€ten zu erfassen. Diese Neuausrichtung ermöglicht es Earables sich als bedeutende Wearable Kategorie zu etablieren, und die Vision von Earables als eine vielseitige Sensorenplattform zur Erweiterung der menschlichen FĂ€higkeiten wird somit zunehmend realer

    Non-Invasive Blood Glucose Monitoring Using Electromagnetic Sensors

    Get PDF
    Monitoring glycemia levels in people with diabetes has developed rapidly over the last decade. A broad range of easy-to-use systems of reliable accuracies are now deployed in the market following the introduction of the invasive self-monitoring blood glucose meters (i.e., Glucometers) that utilize the capillary blood samples from the fingertips of diabetic patients. Besides, semi-invasive continuous monitors (CGM) are currently being used to quantify the glucose analyte in interstitial fluids (ISF) using an implantable needle-like electrochemical sensors. However, the limitations and discomforts associated with these finger-pricking and implantable point-of-care devices have established a new demand for complete non-invasive pain-free and low-cost blood glucose monitors to allow for more frequent and convenient glucose checks and thereby contribute more generously to diabetes care and prevention. Towards that goal, researchers have been developing alternative techniques that are more convenient, affordable, pain-free, and can be used for continuous non-invasive blood glucose monitoring. In this research, a variety of electromagnetic sensing techniques were developed for reliably monitoring the blood glucose levels of clinical relevance to diabetes using the non-ionizing electromagnetic radiations of no hazards when penetrating the body. The sensing structures and devices introduced in this study were designed to operate in specific frequency spectrums that promise a reliable and sensitive glucose detection from centimeter- to millimeter-wave bands. Particularly, three different technologies were proposed and investigated at the Centre for Intelligent Antenna and Radio Systems (CIARS): Complementary Split-Ring Resonators (CSRRs), Whispering Gallery Modes (WGMs) sensors, and Frequency-Modulated Continuous-Wave (FMCW) millimeter-Wave Radars. Multiple sensing devices were developed using those proposed technologies in the micro/millimeter-wave spectrums of interest. A comprehensive study was conducted for the functionality, sensitivity, and repeatability analysis of each sensing device. Particularly, the sensors were thoroughly designed, optimized, fabricated, and practically tested in the laboratory with the desired glucose sensitivity performance. Different topologies and configurations of the proposed sensors were studied and compared in sensitivity using experimental and numerical analysis tools. Besides, machine learning and signal processing tools were intelligently applied to analyze the frequency responses of the sensors and reliably identify different glucose levels. The developed glucose sensors were coupled with frequency-compatible radar boards to realize small mobile glucose sensing systems of reduced cost. The proposed sensors, beside their impressive detection capability of the diabetes-spectrum glucose concentrations, are endowed with favourable advantages of simple fabrication, low-power consumption, miniaturized compact sizing, non-ionizing radiation, and minimum health risk or impact for human beings. Such attractive features promote the proposed sensors as possible candidates for development as mobile, portable/wearable gadgets for affordable non-invasive blood glucose monitoring for diabetes. The introduced sensing structures could also be employed for other vital sensing applications such as liquid type/quantity identification, oil adulteration detection, milk quality control, and virus/bacteria detection. Another focus of this thesis is to investigate the electromagnetic behavior of the glucose in blood mimicking tissues across the microwave spectrum from 200 MHz to 67 GHz using a commercial characterization system (DAK-TL) developed by SPEAG. This is beneficial to locate the promising frequency spectrums that are most responsive to slight variations in glucose concentrations, and to identify the amount of change in the dielectric properties due to different concentrations of interest. Besides, the effect of the blood typing and medication was also investigated by measuring the dielectric properties of synthetic “artificial” as well as authentic “human” blood samples of different ABO-Rh types and with different medications. Measured results have posed for other factors that may impact the developed microwave sensors accuracy and sensitivity including the patient’s blood type, pre-existing medical conditions, or other illnesses

    IoT and Sensor Networks in Industry and Society

    Get PDF
    The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the ïŹrst industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and ïŹ‚exible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

    Sviluppo di un metodo innovativo per la misura del comfort termico attraverso il monitoraggio di parametri fisiologici e ambientali in ambienti indoor

    Get PDF
    openLa misura del comfort termico in ambienti indoor Ăš un argomento di interesse per la comunitĂ  scientifica, poichĂ© il comfort termico incide profondamente sul benessere degli utenti ed inoltre, per garantire condizioni di comfort ottimali, gli edifici devono affrontare costi energetici elevati. Anche se esistono norme nel campo dell'ergonomia del comfort che forniscono linee guida per la valutazione del comfort termico, puĂČ succedere che in contesti reali sia molto difficile ottenere una misurazione accurata. Pertanto, per migliorare la misura del comfort termico negli edifici, la ricerca si sta concentrando sulla valutazione dei parametri personali e fisiologici legati al comfort termico, per creare ambienti su misura per l’utente. Questa tesi presenta diversi contributi riguardo questo argomento. Infatti, in questo lavoro di ricerca, sono stati implementati una serie di studi per sviluppare e testare procedure di misurazione in grado di valutare quantitativamente il comfort termico umano, tramite parametri ambientali e fisiologici, per catturare le peculiaritĂ  che esistono tra i diversi utenti. In primo luogo, Ăš stato condotto uno studio in una camera climatica controllata, con un set di sensori invasivi utilizzati per la misurazione dei parametri fisiologici. L'esito di questa ricerca Ăš stato utile per ottenere una prima accuratezza nella misurazione del comfort termico dell'82%, ottenuta mediante algoritmi di machine learning (ML) che forniscono la sensazione termica (TSV) utilizzando la variabilitĂ  della frequenza cardiaca (HRV) , parametro che la letteratura ha spesso riportato legato sia al comfort termico dell'utenza che alle grandezze ambientali. Questa ricerca ha dato origine a uno studio successivo in cui la valutazione del comfort termico Ăš stata effettuata utilizzando uno smartwatch minimamente invasivo per la raccolta dell’HRV. Questo secondo studio consisteva nel variare le condizioni ambientali di una stanza semi-controllata, mentre i partecipanti potevano svolgere attivitĂ  di ufficio ma in modo limitato, ovvero evitando il piĂč possibile i movimenti della mano su cui era indossato lo smartwatch. Con questa configurazione, Ăš stato possibile stabilire che l'uso di algoritmi di intelligenza artificiale (AI) e il set di dati eterogeneo creato aggregando parametri ambientali e fisiologici, puĂČ fornire una misura di TSV con un errore medio assoluto (MAE) di 1.2 e un errore percentuale medio assoluto (MAPE) del 20%. Inoltre, tramite il Metodo Monte Carlo (MCM) Ăš stato possibile calcolare l'impatto delle grandezze in ingresso sul calcolo del TSV. L'incertezza piĂč alta Ăš stata raggiunta a causa dell'incertezza nella misura della temperatura dell'aria (U = 14%) e dell'umiditĂ  relativa (U = 10,5%). L'ultimo contributo rilevante ottenuto con questa ricerca riguarda la misura del comfort termico in ambiente reale, semi controllato, in cui il partecipante non Ăš stato costretto a limitare i propri movimenti. La temperatura della pelle Ăš stata inclusa nel set-up sperimentale, per migliorare la misurazione del TSV. I risultati hanno mostrato che l'inclusione della temperatura della pelle per la creazione di modelli personalizzati, realizzati utilizzando i dati provenienti dal singolo partecipante, porta a risultati soddisfacenti (MAE = 0,001±0,0003 e MAPE = 0,02%±0,09%). L'approccio piĂč generalizzato, invece, che consiste nell'addestrare gli algoritmi sull'intero gruppo di partecipanti tranne uno, e utilizzare quello tralasciato per il test, fornisce prestazioni leggermente inferiori (MAE = 1±0.2 e MAPE = 25% ±6%). Questo risultato evidenzia come in condizioni semi-controllate, la previsione di TSV utilizzando la temperatura della pelle e l'HRV possa essere eseguita con un certo grado di incertezza.Measuring human thermal comfort in indoor environments is a topic of interest in the scientific community, since thermal comfort deeply affects the well-being of occupants and furthermore, to guarantee optimal comfort conditions, buildings must face high energy costs. Even if there are standards in the field of the ergonomics of the thermal environment that provide guidelines for thermal comfort assessment, it can happen that in real-world settings it is very difficult to obtain an accurate measurement. Therefore, to improve the measurement of thermal comfort of occupants in buildings, research is focusing on the assessment of personal and physiological parameters related to thermal comfort, to create environments carefully tailored to the occupant that lives in it. This thesis presents several contributions to this topic. In fact, in the following research work, a set of studies were implemented to develop and test measurement procedures capable of quantitatively assessing human thermal comfort, by means of environmental and physiological parameters, to capture peculiarities among different occupants. Firstly, it was conducted a study in a controlled climatic chamber with an invasive set of sensors used for measuring physiological parameters. The outcome of this research was helpful to achieve a first accuracy in the measurement of thermal comfort of 82%, obtained by training machine learning (ML) algorithms that provide the thermal sensation vote (TSV) by means of environmental quantities and heart rate variability (HRV), a parameter that literature has often reported being related to both users' thermal comfort. This research gives rise to a subsequent study in which thermal comfort assessment was made by using a minimally invasive smartwatch for collecting HRV. This second study consisted in varying the environmental conditions of a semi-controlled test-room, while participants could carry out light-office activities but in a limited way, i.e. avoiding the movements of the hand on which the smartwatch was worn as much as possible. With this experimental setup, it was possible to establish that the use of artificial intelligence (AI) algorithms (such as random forest or convolutional neural networks) and the heterogeneous dataset created by aggregating environmental and physiological parameters, can provide a measure of TSV with a mean absolute error (MAE) of 1.2 and a mean absolute percentage error (MAPE) of 20%. In addition, by using of Monte Carlo Method (MCM), it was possible to compute the impact of the uncertainty of the input quantities on the computation of the TSV. The highest uncertainty was reached due to the air temperature uncertainty (U = 14%) and relative humidity (U = 10.5%). The last relevant contribution obtained with this research work concerns the measurement of thermal comfort in a real-life setting, semi-controlled environment, in which the participant was not forced to limit its movements. Skin temperature was included in the experimental set-up, to improve the measurement of TSV. The results showed that the inclusion of skin temperature for the creation of personalized models, made by using data coming from the single participant brings satisfactory results (MAE = 0.001±0.0003 and MAPE = 0.02%±0.09%). On the other hand, the more generalized approach, which consists in training the algorithms on the whole bunch of participants except one, and using the one left out for the test, provides slightly lower performances (MAE = 1±0.2 and MAPE = 25%±6%). This result highlights how in semi-controlled conditions, the prediction of TSV using skin temperature and HRV can be performed with acceptable accuracy.INGEGNERIA INDUSTRIALEembargoed_20220321Morresi, Nicol

    Minding the Gap: Computing Ethics and the Political Economy of Big Tech

    Get PDF
    In 1988 Michael Mahoney wrote that “[w]hat is truly revolutionary about the computer will become clear only when computing acquires a proper history, one that ties it to other technologies and thus uncovers the precedents that make its innovations significant” (Mahoney, 1988). Today, over thirty years after this quote was written, we are living right in the middle of the information age and computing technology is constantly transforming modern living in revolutionary ways and in such a high degree that is giving rise to many ethical considerations, dilemmas, and social disruption. To explore the myriad of issues associated with the ethical challenges of computers using the lens of political economy it is important to explore the history and development of computer technology
    • 

    corecore