Predicting Subclinical Atherosclerosis in Low-Risk Individuals Ideal Cardiovascular Health Score and Fuster-BEWAT Score

BACKGROUND The ideal cardiovascular health score (ICHS) is recommended for use in primary prevention. Simpler tools not requiring laboratory tests, such as the Fuster-BEWAT (blood pressure [B], exercise [E], weight [W], alimentation [A], and tobacco [T]) score (FBS), are also available. OBJECTIVES The purpose of this study was to compare the effectiveness of ICHS and FBS in predicting the presence and extent of subclinical atherosclerosis. METHODS A total of 3,983 participants 40 to 54 years of age were enrolled in the PESA (Progression of Early Subclinical Atherosclerosis) cohort. Subclinical atherosclerosis was measured in right and left carotids, abdominal aorta, right and left iliofemoral arteries, and coronary arteries. Subjects were classified as having poor, intermediate, or ideal cardiovascular health based on the number of favorable ICHS or FBS. RESULTS With poor ICHS and FBS as references, individuals with ideal ICHS and FBS showed lower adjusted odds of having atherosclerotic plaques (ICHS odds ratio [OR]: 0.41; 95\% confidence interval [CI]: 0.31 to 0.55 vs. FBS OR: 0.49; 95\% CI: 0.36 to 0.66), coronary artery calcium (CACS) >= 1 (CACS OR: 0.41; 95\% CI: 0.28 to 0.60 vs. CACS OR: 0.53; 95\% CI: 0.38 to 0.74), higher number of affected territories (OR: 0.32; 95\% CI: 0.26 to 0.41 vs. OR: 0.39; 95\% CI: 0.31 to 0.50), and higher CACS level (OR: 0.40; 95\% CI: 0.28 to 0.58 vs. OR: 0.52; 95\% CI: 0.38 to 0.72). Similar levels of significantly discriminating accuracy were found for ICHS and FBS with respect to the presence of plaques (C-statistic: 0.694; 95\% CI: 0.678 to 0.711 vs. 0.692; 95\% CI: 0.676 to 0.709, respectively) and for CACS >= 1 (C-statistic: 0.782; 95\% CI: 0.765 to 0.800 vs. 0.780; 95\% CI: 0.762 to 0.798, respectively). CONCLUSIONS Both scores predict the presence and extent of subclinical atherosclerosis with similar accuracy, highlighting the value of the FBS as a simpler and more affordable score for evaluating the risk of subclinical disease. (C) 2017 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.The PESA study was co-funded by Fundacion Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) and Banco Santander. Funding was also provided by Institute of Health Carlos III (PI15/02019) and European Regional Development Fund. CNIC is supported by the Ministry of Economy, Industry and Competitiveness and Pro CNIC Foundation; and is a Severo Ochoa Center of Excellence (SEV-2015-0505). This work is part of a project that received funding from the European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant 707642 and American Heart Association grant 14SFRN20490315. Dr. Bueno has received research funding from Instituto de Salud Carlos III (PIE16/00021), AstraZeneca, Bristol-Myers Squibb, Janssen, and Novartis; is a consultant for Abbott, AstraZeneca, Bayer, Bristol-Myers Squibb-Pfizer, and Novartis; and has received speakers fees and travel and attendance support from AstraZeneca, Bayer, Bristol-Myers Squibb-Pfizer, Ferrer, Novartis, Servier, and Medscape. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Matthew Budoff, MD, served as Guest Editor for this paper.S

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

THE EFFECTS OF A PRIOR EXERCISE BOUT ON THE ENERGETIC AND CARDIOMETABOLIC RESPONSES TO ACUTE MENTAL STRESS

Background: Mental stress is associated with cardiovascular disease (CVD) risk, but the arterial stiffness and energy expenditure (EE) responses to acute mental stress, and whether prior exercise impacts post-stress cardiometabolic reactivity are not known. The objectives of this dissertation were to assess arterial stiffness and EE responses to acute mental stress and to determine the impact of a prior exercise bout on these responses. Methods: In addition to a meta-analysis on the effects of acute mental stress on arterial stiffness, this dissertation entailed two randomized cross-over studies. Forty recreationally active young adults (18-30 y) were recruited. For Cross-over 1, 20 participants attended two laboratory visits: i) Trier Social Stress Test (arithmetic + speech), and ii) Control. For Cross-over 2, 20 different participants attended two laboratory visits: i) Exercise + Trier Social Stress Test (psychosocial task), and ii) Exercise + Control. Exercise consisted of 25 minutes of moderate-intensity elliptical. Arterial stiffness and EE were measured by pulse-wave velocity (PWV) and indirect calorimetry, respectively. Measurements took place pre, during (EE only), and post condition. Mixed model linear regression assessed condition x time interactions. Results: Meta-analysis: Across 17 trials from 9 studies, exposure to acute mental stress caused arterial stiffness to increase (Standardized Mean Difference: 0.45; p<0.05). Cross-over 1: There was a small interaction (B=0.68 m/s, 95%CI: 0.39, 0.97) for PWV [Stress: 0.81 m/s, Control: 0.15 m/s]. There was also a small interaction (B=0.0010 kcal/kg/min, 95%CI: 0.0004, 0.0015) for EE (Stress: 0.0016 kcal/kg/min, Control:0.0005 kcal/kg/min). Cross-over 2: There was a small interaction (B=0.47 m/s, 95%CI: 0.21, 0.72) for PWV (Stress: 0.43 m/s, Control: -0.05 m/s). For EE, there were small main effects of condition (B=0.0005 kcal/kg/min), 95%CI: 0.0003, 0.0008) and time (B=0.0011 kcal/kg/min, 95%CI: 0.0006, 0.0016). Compared to Cross-over 1, the prior exercise introduced in Cross-over 2 dampened the arterial stiffness and EE responses. Conclusions: Arterial stiffness and EE may be key players in the relationship between acute mental stress and CVD risk, and exercise may beneficially moderate this relationship. Future research examining the stress-CVD paradigm, including potential protective effects of exercise, will be necessary to inform stress-related CVD prevention and treatment efforts.Doctor of Philosoph

The cardiometabolic phenotype of UK South Asian Men

Migrant South Asian populations in Europe, North America the Westernised countries have a greater cardiovascular disease (CVD) risk than their respective indigenous populations. Both overall and premature CVD morbidity and mortality is significantly higher in migrant South Asians than in white populations in the UK and globally. Despite this, the role of ultrasound as a screening tool for CVD risk assessment in South Asians has not been studied extensively. Data also suggest that increased susceptibility to the adverse effects of insulin resistance and type 2 diabetes mellitus (T2DM) may contribute to the increased CVD risk. South Asians living in the United Kingdom also have a 3-5 fold increased prevalence of T2DM, developing the disease around a decade earlier and at a lower body mass index (BMI) compared to white Europeans. Furthermore, non-diabetic South Asians have higher fasting glycaemia and are more insulin resistant than Europeans. Liver fat is also associated with insulin resistance and T2DM risk and is considered to play a causal role in diabetes. Limited data suggest that South Asians have higher liver fat content than age- and BMI-matched Europeans, but it is not currently clear whether this contributes to the observed ethnic difference in insulin resistance. The first aim was to determine the extent to which increased insulin resistance and fasting glycaemia in South Asian, compared to white European men, living in the UK, was due to lower cardiorespiratory fitness (maximal oxygen uptake [VO2max]) and physical activity. The second aim was to determine whether South Asians have increased liver fat compared to Europeans and the extent to which any such differences can explain the increased insulin resistance observed between these groups. The final aim was to determine whether South Asians had a difference in carotid intima-media thickness (cIMT) or carotid plaque presence compared to Europeans; and if so, whether any measured risk factors (if any) could account for any such observed differences in cIMT and/or carotid plaque disease. 100 SA and 100 age and BMI-matched European men without diagnosed diabetes, aged 40-70 years, had fasting blood taken for glucose concentration, insulin, plus other risk factors, and underwent 2-dimensional carotid ultrasound for measurement of intima-media thickness and carotid plaque analysis, assessment of physical activity (using accelerometry), VO2max, body size and composition, and demographic and other lifestyle factors. For addressing the first aim of this thesis, 13 South Asian and 1 European man had HbA1c levels >6.5% indicating potential undiagnosed diabetes and were excluded from the analyses relating. Linear regression models were used to determine the extent to which body size and composition, fitness and physical activity variables explained differences in insulin resistance (assessed by Homeostasis Assessment Model of Insulin Resistance, HOMAIR) and fasting glucose between SA and Europeans. For the second aim, 28 South Asian and 24 European participants were chosen at random (but matched for age) within 4 months of their original main study visit to undergo magnetic resonance spectroscopy for quantification of liver fat. HOMAIR and fasting glucose were 67% (p<0.001) and 3% (p<0.018) higher, respectively, in South Asians than Europeans. Lower VO2max, lower physical activity and greater total adiposity in SA individually explained 68% (95% confidence interval [CI], 45-91%), 29% (95% CI, 11-46%) and 52% (95% CI, 30-80%), respectively, and together explained 83% (95% CI, 50-119%) (all p<0.001), of the ethnic difference in HOMAIR. Lower VO2max and greater total adiposity respectively explained 61% (95% CI, 9-111%) and 39% (95% CI; 9-76%) (combined effect 63% (95% CI 8-115%); all p<0.05)) of the ethnic difference in fasting glucose. Unadjusted mean liver fat content did not differ significantly between South Asians compared to Europeans (5.28 (standard deviation [SD], 2.11)% vs 5.41 (SD,2.35)%, p=0.913), but following adjustment for alcohol consumption was significantly lower in South Asians than Europeans (5.30 (SD, 2.10)% vs 9.03 (SD, 2.22)% p=0.017). Adjustment for alcohol-adjusted liver fat did not attenuate the difference in HOMAIR between ethnic groups. There were no significant differences in unadjusted or age-adjusted in mean cIMT between South Asians and Europeans. There was an increased odds ratio for the presence of plaque disease in South Asians compared to Europeans, however this was not significant (OR 1.57, 95% CI 0.89-2.77, p=0.13). Lower cardiorespiratory fitness is a key factor associated with the excess insulin resistance and fasting glycaemia in middle-aged South Asian compared to European men living in the UK. Also, whilst clear associations between liver fat and insulin resistance were observed in South Asians and Europeans, these results challenge the notion that excess liver fat per se explains the greater insulin resistance observed in South Asians. Finally, cIMT is similar between South Asian and European men and there is also currently no clear evidence for more carotid plaques in South Asian compared to European men living in the UK. This important negative finding highlights the need for further studies on carotid plaque or research in alternative screening methods for CVD which are more sensitive in identifying subclinical CVD

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Extracting clinically-actionable information from wearable physiological monitors

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 161-167).In this thesis I examine several ways of extracting information from wearable monitors so as to help make clinical decisions. Wearable physiological sensors are developing rapidly, and pose a possible part of the solution to the demands of an aging population and rising health care costs. It is important that the data produced by such sensors be processed into information that is clinically relevant and will have an impact on the practice of medicine. I collected data in an ambulatory setting from several wearable physiological sensors, including electrocardiogram, arterial blood pressure, pulse plethysmograph, respiration and acceleration. Using this data set, I demonstrated a few approaches - including signal processing, and algorithms based on the application of physiological models - to extract clinically relevant information. These approaches are potentially of interest to both device makers interested in developing wearable monitors, and to clinicians who will be using such monitors in the future.by Bryan Haslam.S.M

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