Earables: Wearable Computing on the Ears

Kopfhörer haben sich bei Verbrauchern durchgesetzt, da sie private Audiokanäle anbieten, zum Beispiel zum Hören von Musik, zum Anschauen der neuesten Filme während dem Pendeln oder zum freihändigen Telefonieren. Dank diesem eindeutigen primären Einsatzzweck haben sich Kopfhörer im Vergleich zu anderen Wearables, wie zum Beispiel Smartglasses, bereits stärker durchgesetzt. In den letzten Jahren hat sich eine neue Klasse von Wearables herausgebildet, die als "Earables" bezeichnet werden. Diese Geräte sind so konzipiert, dass sie in oder um die Ohren getragen werden können. Sie enthalten verschiedene Sensoren, um die Funktionalität von Kopfhörern zu erweitern. Die räumliche Nähe von Earables zu wichtigen anatomischen Strukturen des menschlichen Körpers bietet eine ausgezeichnete Plattform für die Erfassung einer Vielzahl von Eigenschaften, Prozessen und Aktivitäten. Auch wenn im Bereich der Earables-Forschung bereits einige Fortschritte erzielt wurden, wird deren Potenzial aktuell nicht vollständig abgeschöpft. Ziel dieser Dissertation ist es daher, neue Einblicke in die Möglichkeiten von Earables zu geben, indem fortschrittliche Sensorikansätze erforscht werden, welche die Erkennung von bisher unzugänglichen Phänomenen ermöglichen. Durch die Einführung 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 Fähigkeiten zu etablieren. Um eine fundierte Grundlage für 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-Phänomenen werden in der Dissertation anschließ-end Arbeiten aus verschiedenen Bereichen zusammengefasst, darunter (i) physiologische Überwachung und Gesundheit, (ii) Bewegung und Aktivität, (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 gesünderen Lebensstil zu fördern und eine proaktive Gesundheitsversorgung zu ermöglichen. Darüber hinaus wird in dieser Dissertation ein innovativer Eye-Tracking-Ansatz namens "earEOG" vorgestellt, welcher Aktivitätserkennung 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 Ansätze. Darüber hinaus wird ein Regressionsmodell eingeführt, um absolute Änderungen des Blickwinkels auf der Grundlage von earEOG vorherzusagen. Diese Entwicklung eröffnet neue Möglichkeiten für Forschung, welche sich nahtlos in das tägliche Leben integrieren lässt und tiefere Einblicke in das menschliche Verhalten ermöglicht. Weiterhin zeigt diese Arbeit, wie sich die einzigarte Bauform von Earables mit Sensorik kombinieren lässt, um neuartige Phänomene 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 zusätzliches interaktives Medium eingesetzt, welches eine freihändige 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 mündet die Dissertation in einer offenen Hard- und Software-Plattform für Earables namens "OpenEarable". OpenEarable umfasst eine Reihe fortschrittlicher Sensorfunktionen, die für verschiedene ohrbasierte Forschungsanwendungen geeignet sind, und ist gleichzeitig einfach herzustellen. Hierdurch werden die Einstiegshürden in die ohrbasierte Sensorforschung gesenkt und OpenEarable trägt somit dazu bei, das gesamte Potenzial von Earables auszuschöpfen. Darüber hinaus trägt die Dissertation grundlegenden Designrichtlinien und Referenzarchitekturen für Earables bei. Durch diese Forschung schließt die Dissertation die Lücke 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. Darüber hinaus erweitert diese Dissertation den traditionellen Anwendungsbereich von Kopfhörern, indem sie die auf Audio fokussierten Geräte zu einer Plattform erweitert, welche eine Vielzahl fortschrittlicher Sensorfähigkeiten bietet, um Eigenschaften, Prozesse und Aktivitäten 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 Fähigkeiten wird somit zunehmend realer

Obstacles to wearable computing

In the year 2021, wearable technology could look beautiful and feel magical, but instead is exemplified by a plain wristband that looks suspiciously like a prison monitor. How can we make wearable technology that respects our privacy, enhances our daily lives, integrates with our other connected devices without leashing us to a smartphone, and visually expresses who we are? This study uses a novel method of participatory design fiction (PDFi) to understand potential users of everyday wearable technology through storytelling. I recruited participants from the general public and gave them a five-point prompt to create a design fiction (DF), which inspired the user-centred design of an everyday connected wearable device. The participants each received a technology probe to wear in the wild for a year. They then updated their DFs as a way to reflect on the implications of the technology. For the purposes of privacy, augmenting device functionality through interoperability, and integration into an Internet of Things (IoT) ecosystem, I used the Hub-of-All-Things personal data store to provide the software infrastructure. By listening to their stories, we can elicit design concepts directly from the users, to help us create wearable IoT devices that put the wearer at the centre of the design process, and are satisfying both functionally and emotionally.The Alan Turing Institute Doctoral Scheme, University of Cambridge Department of Computer Science and Technology, The Kenneth Hayter Memorial Fun

Designing Efficient and Accurate Behavior-Aware Mobile Systems

The proliferation of sensors on smartphones, tablets and wearables has led to a plethora of behavior classification algorithms designed to sense various aspects of individual user\u27s behavior such as daily habits, activity, physiology, mobility, sleep, emotional and social contexts. This ability to sense and understand behaviors of mobile users will drive the next generation of mobile applications providing services based on the users\u27 behavioral patterns. In this thesis, we investigate ways in which we can enhance and utilize the understanding of user behaviors in such applications. In particular, we focus on identifying the key challenges in the following three aspects of behavior-aware applications: detection, understanding, and prediction of user behaviors; and present systems and techniques developed to address these challenges. In this thesis, we first demonstrate the utility of wristbands equipped with inertial sensors in real-time detection of health-related behaviors such as smoking and eating. Our approach detects these behaviors in a passive manner without any explicit user interaction and does not require use of any cumbersome device. Our results show that we can detect smoking with 95% accuracy, 91% precision and 81% recall in the natural environment. Second, we design a context-query engine for sensing multiple user contexts continuously, accurately and efficiently on mobile devices; the key necessity for understanding and analyzing behaviors. Our context-query engine performs information fusion of contexts for an individual user to enable optimizations like i) energy-efficient sensing, and ii) accurate context inference. Our results show that we can improve accuracy of a context classifier by up to 42% and reduce the number of classifiers required to observe the user state by 33%. Finally, we demonstrate the utility of predicting app usage behavior, in improving the freshness of mobile apps such as Facebook that present users with the latest content fetched from remote servers. We present an app prediction algorithm that utilizes user contexts to predict the app a user is likely to use and pre-fetches the data over the network for the predicted app. We show that our proposed algorithm delivers application content to the user that is on an average fresh within 3 minutes

Computational Sleep Behaviour Analysis and Application

Sleep affects a person’s health and is, therefore, assessed if health problems arise. Sleep behaviour is monitored for abnormalities in order to determine if any treatments, such as medication or behavioural changes (modifications to sleep habits), are necessary. Assessments are typically done using two methods: polysomnography over short periods and four-week retrospective questionnaires. These standard methods, however, cannot measure current sleep status continuously and unsupervised over long periods of time in the same way home-based sleep behaviour assessment can. In this work, we investigate the ability of sleep behaviour assessment using IoT devices in a natural home environment, which potential has not been investigated fully, to enable early abnormality detection and facilitate self-management. We developed a framework that incorporates different facets and perspectives to introduce focus and support in sleep behaviour assessment. The framework considers users’ needs, various available technologies, and factors that influence sleep behaviours. Sleep analysis approaches are incorporated to increase the reliability of the system. This assessment is strengthened by utilising sleep stage detection and sleep position recognition. This includes, first, the extraction and integration of influence factors of sleep stage recognition methods to create a fine-grained personalised approach and, second, the detection of common but more complex sleep positions, including leg positions. The relations between medical conditions and sleep are assessed through interviews with doctors and users on various topics, including treatment satisfaction and technology acceptance. The findings from these interviews led to the investigation of sleep behaviour as a diagnostic indicator. Changes in sleep behaviour are assessed alongside medical knowledge using data mining techniques to extract information about disease development; the following diseases were of interest: sleep apnoea, hypertension, diabetes, and chronic kidney disease. The proposed framework is designed in a way that allows it to be integrated into existing smart home environments. We believe that our framework provides promising building blocks for reliable sleep behaviour assessment by incorporating newly developed sleep analysis approaches. These approaches include a modular layered sleep behaviour assessment framework, a sleep regularity algorithm, a user-dependent visualisation concept, a higher-granularity sleep position analysis approach, a fine-grained sleep stage detection approach, a personalised sleep parameter extraction process, in-depth understanding on sleep and chronic disease relations, and a sleep-wake behaviour-based chronic disease detection method.This work has been supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 676157

Grasp-sensitive surfaces

Grasping objects with our hands allows us to skillfully move and manipulate them. Hand-held tools further extend our capabilities by adapting precision, power, and shape of our hands to the task at hand. Some of these tools, such as mobile phones or computer mice, already incorporate information processing capabilities. Many other tools may be augmented with small, energy-efficient digital sensors and processors. This allows for graspable objects to learn about the user grasping them - and supporting the user's goals. For example, the way we grasp a mobile phone might indicate whether we want to take a photo or call a friend with it - and thus serve as a shortcut to that action. A power drill might sense whether the user is grasping it firmly enough and refuse to turn on if this is not the case. And a computer mouse could distinguish between intentional and unintentional movement and ignore the latter. This dissertation gives an overview of grasp sensing for human-computer interaction, focusing on technologies for building grasp-sensitive surfaces and challenges in designing grasp-sensitive user interfaces. It comprises three major contributions: a comprehensive review of existing research on human grasping and grasp sensing, a detailed description of three novel prototyping tools for grasp-sensitive surfaces, and a framework for analyzing and designing grasp interaction: For nearly a century, scientists have analyzed human grasping. My literature review gives an overview of definitions, classifications, and models of human grasping. A small number of studies have investigated grasping in everyday situations. They found a much greater diversity of grasps than described by existing taxonomies. This diversity makes it difficult to directly associate certain grasps with users' goals. In order to structure related work and own research, I formalize a generic workflow for grasp sensing. It comprises *capturing* of sensor values, *identifying* the associated grasp, and *interpreting* the meaning of the grasp. A comprehensive overview of related work shows that implementation of grasp-sensitive surfaces is still hard, researchers often are not aware of related work from other disciplines, and intuitive grasp interaction has not yet received much attention. In order to address the first issue, I developed three novel sensor technologies designed for grasp-sensitive surfaces. These mitigate one or more limitations of traditional sensing techniques: **HandSense** uses four strategically positioned capacitive sensors for detecting and classifying grasp patterns on mobile phones. The use of custom-built high-resolution sensors allows detecting proximity and avoids the need to cover the whole device surface with sensors. User tests showed a recognition rate of 81%, comparable to that of a system with 72 binary sensors. **FlyEye** uses optical fiber bundles connected to a camera for detecting touch and proximity on arbitrarily shaped surfaces. It allows rapid prototyping of touch- and grasp-sensitive objects and requires only very limited electronics knowledge. For FlyEye I developed a *relative calibration* algorithm that allows determining the locations of groups of sensors whose arrangement is not known. **TDRtouch** extends Time Domain Reflectometry (TDR), a technique traditionally used for inspecting cable faults, for touch and grasp sensing. TDRtouch is able to locate touches along a wire, allowing designers to rapidly prototype and implement modular, extremely thin, and flexible grasp-sensitive surfaces. I summarize how these technologies cater to different requirements and significantly expand the design space for grasp-sensitive objects. Furthermore, I discuss challenges for making sense of raw grasp information and categorize interactions. Traditional application scenarios for grasp sensing use only the grasp sensor's data, and only for mode-switching. I argue that data from grasp sensors is part of the general usage context and should be only used in combination with other context information. For analyzing and discussing the possible meanings of grasp types, I created the GRASP model. It describes five categories of influencing factors that determine how we grasp an object: *Goal* -- what we want to do with the object, *Relationship* -- what we know and feel about the object we want to grasp, *Anatomy* -- hand shape and learned movement patterns, *Setting* -- surrounding and environmental conditions, and *Properties* -- texture, shape, weight, and other intrinsics of the object I conclude the dissertation with a discussion of upcoming challenges in grasp sensing and grasp interaction, and provide suggestions for implementing robust and usable grasp interaction.Die Fähigkeit, Gegenstände mit unseren Händen zu greifen, erlaubt uns, diese vielfältig zu manipulieren. Werkzeuge erweitern unsere Fähigkeiten noch, indem sie Genauigkeit, Kraft und Form unserer Hände an die Aufgabe anpassen. Digitale Werkzeuge, beispielsweise Mobiltelefone oder Computermäuse, erlauben uns auch, die Fähigkeiten unseres Gehirns und unserer Sinnesorgane zu erweitern. Diese Geräte verfügen bereits über Sensoren und Recheneinheiten. Aber auch viele andere Werkzeuge und Objekte lassen sich mit winzigen, effizienten Sensoren und Recheneinheiten erweitern. Dies erlaubt greifbaren Objekten, mehr über den Benutzer zu erfahren, der sie greift - und ermöglicht es, ihn bei der Erreichung seines Ziels zu unterstützen. Zum Beispiel könnte die Art und Weise, in der wir ein Mobiltelefon halten, verraten, ob wir ein Foto aufnehmen oder einen Freund anrufen wollen - und damit als Shortcut für diese Aktionen dienen. Eine Bohrmaschine könnte erkennen, ob der Benutzer sie auch wirklich sicher hält und den Dienst verweigern, falls dem nicht so ist. Und eine Computermaus könnte zwischen absichtlichen und unabsichtlichen Mausbewegungen unterscheiden und letztere ignorieren. Diese Dissertation gibt einen Überblick über Grifferkennung (*grasp sensing*) für die Mensch-Maschine-Interaktion, mit einem Fokus auf Technologien zur Implementierung griffempfindlicher Oberflächen und auf Herausforderungen beim Design griffempfindlicher Benutzerschnittstellen. Sie umfasst drei primäre Beiträge zum wissenschaftlichen Forschungsstand: einen umfassenden Überblick über die bisherige Forschung zu menschlichem Greifen und Grifferkennung, eine detaillierte Beschreibung dreier neuer Prototyping-Werkzeuge für griffempfindliche Oberflächen und ein Framework für Analyse und Design von griff-basierter Interaktion (*grasp interaction*). Seit nahezu einem Jahrhundert erforschen Wissenschaftler menschliches Greifen. Mein Überblick über den Forschungsstand beschreibt Definitionen, Klassifikationen und Modelle menschlichen Greifens. In einigen wenigen Studien wurde bisher Greifen in alltäglichen Situationen untersucht. Diese fanden eine deutlich größere Diversität in den Griffmuster als in existierenden Taxonomien beschreibbar. Diese Diversität erschwert es, bestimmten Griffmustern eine Absicht des Benutzers zuzuordnen. Um verwandte Arbeiten und eigene Forschungsergebnisse zu strukturieren, formalisiere ich einen allgemeinen Ablauf der Grifferkennung. Dieser besteht aus dem *Erfassen* von Sensorwerten, der *Identifizierung* der damit verknüpften Griffe und der *Interpretation* der Bedeutung des Griffes. In einem umfassenden Überblick über verwandte Arbeiten zeige ich, dass die Implementierung von griffempfindlichen Oberflächen immer noch ein herausforderndes Problem ist, dass Forscher regelmäßig keine Ahnung von verwandten Arbeiten in benachbarten Forschungsfeldern haben, und dass intuitive Griffinteraktion bislang wenig Aufmerksamkeit erhalten hat. Um das erstgenannte Problem zu lösen, habe ich drei neuartige Sensortechniken für griffempfindliche Oberflächen entwickelt. Diese mindern jeweils eine oder mehrere Schwächen traditioneller Sensortechniken: **HandSense** verwendet vier strategisch positionierte kapazitive Sensoren um Griffmuster zu erkennen. Durch die Verwendung von selbst entwickelten, hochauflösenden Sensoren ist es möglich, schon die Annäherung an das Objekt zu erkennen. Außerdem muss nicht die komplette Oberfläche des Objekts mit Sensoren bedeckt werden. Benutzertests ergaben eine Erkennungsrate, die vergleichbar mit einem System mit 72 binären Sensoren ist. **FlyEye** verwendet Lichtwellenleiterbündel, die an eine Kamera angeschlossen werden, um Annäherung und Berührung auf beliebig geformten Oberflächen zu erkennen. Es ermöglicht auch Designern mit begrenzter Elektronikerfahrung das Rapid Prototyping von berührungs- und griffempfindlichen Objekten. Für FlyEye entwickelte ich einen *relative-calibration*-Algorithmus, der verwendet werden kann um Gruppen von Sensoren, deren Anordnung unbekannt ist, semi-automatisch anzuordnen. **TDRtouch** erweitert Time Domain Reflectometry (TDR), eine Technik die üblicherweise zur Analyse von Kabelbeschädigungen eingesetzt wird. TDRtouch erlaubt es, Berührungen entlang eines Drahtes zu lokalisieren. Dies ermöglicht es, schnell modulare, extrem dünne und flexible griffempfindliche Oberflächen zu entwickeln. Ich beschreibe, wie diese Techniken verschiedene Anforderungen erfüllen und den *design space* für griffempfindliche Objekte deutlich erweitern. Desweiteren bespreche ich die Herausforderungen beim Verstehen von Griffinformationen und stelle eine Einteilung von Interaktionsmöglichkeiten vor. Bisherige Anwendungsbeispiele für die Grifferkennung nutzen nur Daten der Griffsensoren und beschränken sich auf Moduswechsel. Ich argumentiere, dass diese Sensordaten Teil des allgemeinen Benutzungskontexts sind und nur in Kombination mit anderer Kontextinformation verwendet werden sollten. Um die möglichen Bedeutungen von Griffarten analysieren und diskutieren zu können, entwickelte ich das GRASP-Modell. Dieses beschreibt fünf Kategorien von Einflussfaktoren, die bestimmen wie wir ein Objekt greifen: *Goal* -- das Ziel, das wir mit dem Griff erreichen wollen, *Relationship* -- das Verhältnis zum Objekt, *Anatomy* -- Handform und Bewegungsmuster, *Setting* -- Umgebungsfaktoren und *Properties* -- Eigenschaften des Objekts, wie Oberflächenbeschaffenheit, Form oder Gewicht. Ich schließe mit einer Besprechung neuer Herausforderungen bei der Grifferkennung und Griffinteraktion und mache Vorschläge zur Entwicklung von zuverlässiger und benutzbarer Griffinteraktion

Wearable computing and contextual awareness

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (leaves 231-248).Computer hardware continues to shrink in size and increase in capability. This trend has allowed the prevailing concept of a computer to evolve from the mainframe to the minicomputer to the desktop. Just as the physical hardware changes, so does the use of the technology, tending towards more interactive and personal systems. Currently, another physical change is underway, placing computational power on the user's body. These wearable machines encourage new applications that were formerly infeasible and, correspondingly, will result in new usage patterns. This thesis suggests that the fundamental improvement offered by wearable computing is an increased sense of user context. I hypothesize that on-body systems can sense the user's context with little or no assistance from environmental infrastructure. These body-centered systems that "see" as the user sees and "hear" as the user hears, provide a unique "first-person" viewpoint of the user's environment. By exploiting models recovered by these systems, interfaces are created which require minimal directed action or attention by the user. In addition, more traditional applications are augmented by the contextual information recovered by these systems. To investigate these issues, I provide perceptually sensible tools for recovering and modeling user context in a mobile, everyday environment. These tools include a downward-facing, camera-based system for establishing the location of the user; a tag-based object recognition system for augmented reality; and several on-body gesture recognition systems to identify various user tasks in constrained environments. To address the practicality of contextually-aware wearable computers, issues of power recovery, heat dissipation, and weight distribution are examined. In addition, I have encouraged a community of wearable computer users at the Media Lab through design, management, and support of hardware and software infrastructure. This unique community provides a heightened awareness of the use and social issues of wearable computing. As much as possible, the lessons from this experience will be conveyed in the thesis.by Thad Eugene Starner.Ph.D

Novel Approaches to Pervasive and Remote Sensing in Cardiovascular Disease Assessment

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, responsible for 45% of all deaths. Nevertheless, their mortality is decreasing in the last decade due to better prevention, diagnosis, and treatment resources. An important medical instrument for the latter processes is the Electrocardiogram (ECG). The ECG is a versatile technique used worldwide for its ease of use, low cost, and accessibility, having evolved from devices that filled up a room, to small patches or wrist- worn devices. Such evolution allowed for more pervasive and near-continuous recordings. The analysis of an ECG allows for studying the functioning of other physiological systems of the body. One such is the Autonomic Nervous System (ANS), responsible for controlling key bodily functions. The ANS can be studied by analyzing the characteristic inter-beat variations, known as Heart Rate Variability (HRV). Leveraging this relation, a pilot study was developed, where HRV was used to quantify the contribution of the ANS in modulating cardioprotection offered by an experimental medical procedure called Remote Ischemic Conditioning (RIC), offering a more objective perspective. To record an ECG, electrodes are responsible for converting the ion-propagated action potential to electrons, needed to record it. They are produced from different materials, including metal, carbon-based, or polymers. Also, they can be divided into wet (if an elec- trolyte gel is used) or dry (if no added electrolyte is used). Electrodes can be positioned either inside the body (in-the-person), attached to the skin (on-the-body), or embedded in daily life objects (off-the-person), with the latter allowing for more pervasive recordings. To this effect, a novel mobile acquisition device for recording ECG rhythm strips was developed, where polymer-based embedded electrodes are used to record ECG signals similar to a medical-grade device. One drawback of off-the-person solutions is the increased noise, mainly caused by the intermittent contact with the recording surfaces. A new signal quality metric was developed based on delayed phase mapping, a technique that maps time series to a two-dimensional space, which is then used to classify a segment into good or noisy. Two different approaches were developed, one using a popular image descriptor, the Hu image moments; and the other using a Convolutional Neural Network, both with promising results for their usage as signal quality index classifiers.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo, res- ponsáveis por 45% de todas estas. No entanto, a sua mortalidade tem vindo a diminuir na última década, devido a melhores recursos na prevenção, diagnóstico e tratamento. Um instrumento médico importante para estes recursos é o Eletrocardiograma (ECG). O ECG é uma técnica versátil utilizada em todo o mundo pela sua facilidade de uso, baixo custo e acessibilidade, tendo evoluído de dispositivos que ocupavam uma sala inteira para pequenos adesivos ou dispositivos de pulso. Tal evolução permitiu aquisições mais pervasivas e quase contínuas. A análise de um ECG permite estudar o funcionamento de outros sistemas fisiológi- cos do corpo. Um deles é o Sistema Nervoso Autônomo (SNA), responsável por controlar as principais funções corporais. O SNA pode ser estudado analisando as variações inter- batidas, conhecidas como Variabilidade da Frequência Cardíaca (VFC). Aproveitando essa relação, foi desenvolvido um estudo piloto, onde a VFC foi utilizada para quantificar a contribuição do SNA na modulação da cardioproteção oferecida por um procedimento mé- dico experimental, denominado Condicionamento Isquêmico Remoto (CIR), oferecendo uma perspectiva mais objetiva. Na aquisição de um ECG, os elétrodos são os responsáveis por converter o potencial de ação propagado por iões em eletrões, necessários para a sua recolha. Estes podem ser produzidos a partir de diferentes materiais, incluindo metal, à base de carbono ou polímeros. Além disso, os elétrodos podem ser classificados em húmidos (se for usado um gel eletrolítico) ou secos (se não for usado um eletrólito adicional). Os elétrodos podem ser posicionados dentro do corpo (dentro-da-pessoa), colocados em contacto com a pele (na-pessoa) ou embutidos em objetos da vida quotidiana (fora-da-pessoa), sendo que este último permite gravações mais pervasivas . Para este efeito, foi desenvolvido um novo dispositivo de aquisição móvel para gravar sinal de ECG, onde elétrodos embutidos à base de polímeros são usados para recolher sinais de ECG semelhantes a um dispositivo de grau médico. Uma desvantagem das soluções onde os elétrodos estão embutidos é o aumento do ruído, causado principalmente pelo contato intermitente com as superfícies de aquisição. Uma nova métrica de qualidade de sinal foi desenvolvida com base no mapeamento de fase atrasada, uma técnica que mapeia séries temporais para um espaço bidimensional, que é então usado para classificar um segmento em bom ou ruidoso. Duas abordagens diferentes foram desenvolvidas, uma usando um popular descritor de imagem, e outra utilizando uma Rede Neural Convolucional, com resultados promissores para o seu uso como classificadores de qualidade de sinal

Wearable and automotive systems for affect recognition from physiology

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 152-158).Novel systems and algorithms have been designed and built to recognize affective patterns in physiological signals. Experiments were conducted for evaluation of the new systems and algorithms in three types of settings: a highly constrained laboratory setting, a largely unconstrained ambulatory environment, and a less unconstrained automotive environment. The laboratory experiment was designed to test for the presence of unique physiological patterns in each of eight different emotions given a relatively motionless seated subject, intentionally feeling and expressing these states. This experiment generated a large dataset of physiological signals containing many day-to-day variations, and the proposed features contributed to a success rate of 81% for discriminating all eight emotions and rates of up to 100% for subsets of emotion based on similar emotion qualities. New wearable computer systems and sensors were developed and tested on subjects who walked, jogged, talked, and otherwise went about daily activities. Although in the unconstrained ambulatory setting, physical motion often overwhelmed affective signals, the systems developed in this thesis are currently useful as activity monitors, providing an image diary correlated with physiological signals. Automotive systems were used to detect physiological stress during the natural but physically driving task. This generated a large database of physiological signals covering over 36 hours of driving. Algorithms for detecting driver stress achieved a recognition rates of 96% using stress ratings based on task conditions for validation and 89% accuracy using questionnaires analysis for validation. Further results in which metrics of stress from video tape annotations of the drive were correlated with physiological features showed highly significant correlations (up to r = .77 for over 4000 samples). Together, these three experiments show a range of success in recognizing affect from physiology, showing high recognition rates in somewhat constrained conditions and highlighting the need for more automatic context sensing in unconmore automatic context sensing in unconstrained conditions. The recognition rates obtained thus far lend support to the hypothesis that many emotional differences can be automatically discriminated in patterns of physiological changes.by Jennifer A. Healey.Ph.D