AI Modeling Approaches for Detecting, Characterizing, and Predicting Brief Daily Behaviors such as Toothbrushing using Wrist Trackers.

Continuous advancements in wrist-worn sensors have opened up exciting possibilities for real-time monitoring of individuals\u27 daily behaviors, with the aim of promoting healthier, more organized, and efficient lives. Understanding the duration of specific daily behaviors has become of interest to individuals seeking to optimize their lifestyles. However, there is still a research gap when it comes to monitoring short-duration behaviors that have a significant impact on health using wrist-worn inertial sensors in natural environments. These behaviors often involve repetitive micro-events that last only a few seconds or even microseconds, making their detection and analysis challenging. Furthermore, these micro-events are often surrounded by non-repetitive boundary events, further complicating the identification process. Effective detection and timely intervention during these short-duration behaviors are crucial for designing personalized interventions that can positively impact individuals\u27 lifestyles. To address these challenges, this dissertation introduces three models: mORAL, mTeeth, and Brushing Prompt. These models leverage wrist-worn inertial sensors to accurately infer short-duration behaviors, identify repetitive micro-behaviors, and provide timely interventions related to oral hygiene. The dissertation\u27s contributions extend beyond the development of these models. Firstly, precise and detailed labels for each brief and micro-repetitive behavior are acquired to train and validate the models effectively. This involved meticulous marking of the exact start and end times of each event, including any intervening pauses, at a second-level granularity. A comprehensive scientific research study was conducted to collect such data from participants in their free-living natural environments. Secondly, a solution is proposed to address the issue of sensor placement variability. Given the different positions of the sensor within a wristband and variations in wristband placement on the wrist, the model needs to determine the relative configuration of the inertial sensor accurately. Accurately determining the relative positioning of the inertial sensor with respect to the wrist is crucial for the model to determine the orientation of the hand. Additionally, time synchronization errors between sensor data and associated video, despite both being collected on the same smartphone, are addressed through the development of an algorithm that tightly synchronizes the two data sources without relying on an explicit anchor event. Furthermore, an event-based approach is introduced to identify candidate segments of data for applying machine learning models, outperforming the traditional fixed window-based approach. These candidate segments enable reliable detection of brief daily behaviors in a computationally efficient manner suitable for real-time. The dissertation also presents a computationally lightweight method for identifying anchor events using wrist-worn inertial sensors. Anchor events play a vital role in assigning unambiguous labels in a fixed-length window-based approach to data segmentation and effectively demarcating transitions between micro-repetitive events. Significant features are extracted, and explainable machine learning models are developed to ensure reliable detection of brief daily and micro-repetitive behaviors. Lastly, the dissertation addresses the crucial factor of the opportune moment for intervention during brief daily behaviors using wrist-worn inertial sensors. By leveraging these sensors, users can receive timely and personalized interventions to enhance their performance and improve their lifestyles. Overall, this dissertation makes substantial contributions to the field of real-time monitoring of short-duration behaviors. It tackles various technical challenges, provides innovative solutions, and demonstrates the potential for wrist-worn sensors to facilitate effective interventions and promote healthier behaviors. By advancing our understanding of these behaviors and optimizing intervention strategies, this research has the potential to significantly impact individuals\u27 well-being and contribute to the development of personalized health solutions

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

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

Assessing the quality of mobile apps for oral health: content analysis and usability

AIM: To review and characterize dental mobile apps in the peer-reviewed literature and two popular mobile app stores, and assess their quality. METHODS: A scoping review methodology was used to identify different types of mobile dental apps in seven medical and technical databases. The data of the identified research studies on dental apps were extracted (coded) by two researchers in a systematic process and the results were analyzed. Dental apps’ characteristics, publication pattern, development methodology, efficacy, and usability were reported. Then, a systematic review was conducted in Google Play and iOS app stores to identify patient-facing dental apps. Popular keywords were identified from multiple sources such as using Google Trends. Two researchers extracted the information of the identified dental apps and descriptive, and correlation analyses were conducted. Apps were evaluated for the presence of behavior change techniques (BCTs), evidence-based guidelines, technological features, privacy and security, credibility and health literacy. Lastly, a quality assessment using MARS scale was conducted for a selected sample of dental patient-facing apps from the published literature and the popular app stores. RESULTS: The search resulted in 38 studies on dental apps. The identified dental apps (37) targeted multiple users and the common topic was oral pathology such as dental caries. The majority of dental apps was developed for diagnostics and screening purposes (40.5%). Apps included varied features and educational strategies. Only two papers used theories for developing dental apps, and five studies followed user-centered design principles. Fourteen papers only reported the use of clinical guidelines for developing dental apps (36.8%). Mobile app search resulted in 52 patient-facing dental apps (37 Android apps, and 15 iOS apps). The majority of apps focused on Oral Hygiene Behavior (31%). Apps targeted multiple users and contained varied features and BCTs including instructions (80.8%) and credible source (67.3%). Eleven apps were identified for the third project and the highest total MARS score was Philips Sonicare. The app contained the largest number of features compared to other apps (24). CONCLUSION: The dissertation will warrant the selection and prescription of high-quality dental apps, and will advance the research work in mobile apps in dentistry

Manual of therapeutic stomatology. Pt. 2

СТОМАТОЛОГИЯ ТЕРАПЕВТИЧЕСКАЯРУКОВОДСТВАИНОСТРАННЫЕ СТУДЕНТЫСТОМАТОЛОГИЯ ЛЕЧЕБНО-ВОССТАНОВИТЕЛЬНАЯСТОМАТОГНАТИЧЕСКИЕ БОЛЕЗНИ /ТЕРАПИЯСТОМАТОЛОГИЯУЧЕБНО-МЕТОДИЧЕСКИЕ ПОСОБИЯВ пособии освещены вопросы лечения стоматологических болезней

Aeronautical engineering. A continuing bibliography with indexes, supplement 102

This bibliography lists 326 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1978

Current Treatment of Cleft Lip and Palate

Non-syndromic cleft lip and palate patients have the most common congenital anomalies that affect the middle and lower part of the face, and which impair aesthetic integrity. These anomalies also affect basic functions such as hearing, speaking, chewing, and breathing. Treatment of patients with cleft lip and palate requires multidisciplinary teamwork. The cleft child should be followed and treated by a team of experts from different disciplines who work well together. Although cleft surgery, orthodontic treatment, and speech therapy are the main disciplines, each of the other branches (maxilla-facial-surgeon, otolaryngologist, speech therapist, pediatric dentist, psychologist, nurse etc.) are also very important. In this book, we wanted to present the reader with the experiences and knowledge of some of these disciplines. This book also includes information on the quality of life of children with this anomaly and anesthesia evaluation, which is very important for the cleft surgery