Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Multi-sensor Framework for Heart Rate and Blood Oxygen Saturation Monitoring of Human Body

Cardiovascular diseases have been the cause of death for millions of people. Some of these deaths could be avoided if there was a signi cant increase of diagnosis for the detection of such diseases. This diagnosis, in turn, could be realized with the increased availability of robust and low-cost medical diagnostic devices. Integrated technology sensors available on wearable devices have been commonly used to read physiological data in users (patients). Particularly the pulse oximetry sensors, o ers a unique, non-invasive method that can be used to detect the severity of such diseases. This evaluation of the physical condition of the patient for certain diseases is possible due to non-invasive measurement through photoplethysmography, which allows the extraction of heart rate and oxygen saturation in the blood. Since some diseases diagnoses require simultaneous monitoring of blood oxygen saturation values at various sites in the body, a project has been developed to perform such reading of physiological data. This thesis presents the development of a systems platform based on the use of multiple pulse oximetry sensors connected to an application developed for a mobile device though a wireless connection. The purpose of this platform is to provide an easy-to-read experience of health data that can be analyzed to diagnose cardiovascular disease symptoms, aiding in an early diagnosis. The complete structure as well as the aspects of the analysis and implementation of the systems related to the proposed architecture are described in this dissertation

Sensing via signal analysis, analytics, and cyberbiometric patterns

Includes bibliographical references.2022 Fall.Internet-connected, or Internet of Things (IoT), sensor technologies have been increasingly incorporated into everyday technology and processes. Their functions are situationally dependent and have been used for vital recordings such as electrocardiograms, gait analysis and step counting, fall detection, and environmental analysis. For instance, environmental sensors, which exist through various technologies, are used to monitor numerous domains, including but not limited to pollution, water quality, and the presence of biota, among others. Past research into IoT sensors has varied depending on the technology. For instance, previous environmental gas sensor IoT research has focused on (i) the development of these sensors for increased sensitivity and increased lifetimes, (ii) integration of these sensors into sensor arrays to combat cross-sensitivity and background interferences, and (iii) sensor network development, including communication between widely dispersed sensors in a large-scale environment. IoT inertial measurement units (IMU's), such as accelerometers and gyroscopes, have been previously researched for gait analysis, movement detection, and gesture recognition, which are often related to human-computer interface (HCI). Methods of IoT Device feature-based pattern recognition for machine learning (ML) and artificial intelligence (AI) are frequently investigated as well, including primitive classification methods and deep learning techniques. The result of this research gives insight into each of these topics individually, i.e., using a specific sensor technology to detect carbon monoxide in an indoor environment, or using accelerometer readings for gesture recognition. Less research has been performed on analyzing the systems aspects of the IoT sensors themselves. However, an important part of attaining overall situational awareness is authenticating the surroundings, which in the case of IoT means the individual sensors, humans interacting with the sensors, and other elements of the surroundings. There is a clear opportunity for the systematic evaluation of the identity and performance of an IoT sensor/sensor array within a system that is to be utilized for "full situational awareness". This awareness may include (i) non-invasive diagnostics (i.e., what is occurring inside the body), (ii) exposure analysis (i.e., what has gone into the body through both respiratory and eating/drinking pathways), and (iii) potential risk of exposure (i.e., what the body is exposed to environmentally). Simultaneously, the system has the capability to harbor security measures through the same situational assessment in the form of multiple levels of biometrics. Through the interconnective abilities of the IoT sensors, it is possible to integrate these capabilities into one portable, hand-held system. The system will exist within a "magic wand", which will be used to collect the various data needed to assess the environment of the user, both inside and outside of their bodies. The device can also be used to authenticate the user, as well as the system components, to discover potential deception within the system. This research introduces levels of biometrics for various scenarios through the investigation of challenge-based biometrics; that is, biometrics based upon how the sensor, user, or subject of study responds to a challenge. These will be applied to multiple facets surrounding "situational awareness" for living beings, non-human beings, and non-living items or objects (which we have termed "abiometrics"). Gesture recognition for intent of sensing was first investigated as a means of deliberate activation of sensors/sensor arrays for situational awareness while providing a level of user authentication through biometrics. Equine gait analysis was examined next, and the level of injury in the lame limbs of the horse was quantitatively measured and classified using data from IoT sensors. Finally, a method of evaluating the identity and health of a sensor/sensory array was examined through different challenges to their environments

A new model for the generation of photoplethysmographic signal with its application to the analysis of beat-to-beat blood pressure variability.

Gu Yingying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 155-164).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- IPFM Model --- p.1Chapter 1.1.1 --- Description of IPFM Model --- p.1Chapter 1.1.2 --- Background of IPFM Related Modeling --- p.3Chapter 1.2 --- Windkessel Model --- p.8Chapter 1.2.1 --- Background of the Windkessel Model --- p.8Chapter 1.2.2 --- Windkessel Related Modeling --- p.13Chapter 1.3 --- Photoplethysmogram (PPG) --- p.14Chapter 1.3.1 --- Principle of PPG --- p.14Chapter 1.3.2 --- Characteristics of PPG Signal --- p.16Chapter 1.4 --- A Study on the Beat-to-Beat BPV --- p.18Chapter 1.5 --- Main Purposes of the Study --- p.19Chapter 1.6 --- Organization of the Thesis --- p.20Chapter 2 --- Spectral Analysis on the IPFM Process --- p.22Chapter 2.1 --- Introduction --- p.22Chapter 2.2 --- A Theoretical Study on the Neural Firing Rate Function --- p.23Chapter 2.2.1 --- Mathematical Derivation of the Neural Firing Rate --- p.23Chapter 2.2.2 --- Spectral Analysis of the IPFM Process --- p.27Chapter 2.2.3 --- Reconstruction of Neural Firing Rate through LPF --- p.30Chapter 2.3 --- Effects of Neural Dynamics --- p.33Chapter 2.4 --- Discussion & Conclusion --- p.35Chapter 3 --- A New Model for the Generation of PPG --- p.37Chapter 3.1 --- Introduction --- p.37Chapter 3.2 --- Principles of PPG --- p.38Chapter 3.2.1 --- Relationship between Pressure and Flow --- p.38Chapter 3.2.2 --- Peripheral Pressure and Flow Curves --- p.41Chapter 3.2.3 --- Generation of PPG signal --- p.43Chapter 3.3 --- Model Description --- p.44Chapter 3.3.1 --- IPFM model --- p.45Chapter 3.3.2 --- Windkessel model --- p.46Chapter 3.3.3 --- New Model for the Generation of PPG --- p.49Chapter 3.4 --- Simulation --- p.51Chapter 3.4.1 --- Generation of ECG --- p.51Chapter 3.4.2 --- Generation of PPG --- p.57Chapter 3.4.3 --- Effects of the Modulation Depth on the Output --- p.65Chapter 3.4.4 --- Effects of Mean Autonomic Tone on HRV --- p.72Chapter 3.5 --- Discussion & Conclusion --- p.75Chapter 4 --- A Correlation Study on the Beat-to-Beat Features of Photoplethysmographic Signals --- p.80Chapter 4.1 --- Introduction --- p.80Chapter 4.2 --- Methodology --- p.81Chapter 4.2.1 --- Experimental Conditions --- p.81Chapter 4.2.2 --- Definition of the Parameters --- p.82Chapter 4.3 --- Data Analysis --- p.85Chapter 4.3.1 --- At Normal Relaxed State --- p.85Chapter 4.3.2 --- At Different Levels of Contacting Force --- p.87Chapter 4.3.3 --- At Different Levels of Local Skin Finger Temperature --- p.90Chapter 4.3.4 --- At Dynamic State --- p.93Chapter 4.3.5 --- Repeatability Study --- p.95Chapter 4.3.6 --- Spectral Analysis --- p.96Chapter 4.4 --- Discussion --- p.98Chapter 5 --- The Estimation of the Beat-to-Beat Blood Pressure Variability --- p.103Chapter 5.1 --- Introduction --- p.103Chapter 5.2 --- BP Estimation using FY Interval --- p.104Chapter 5.2.1 --- Multi-Beat BP Estimation under Different Levels of Contacting Force --- p.104Chapter 5.2.2 --- Beat-to-Beat BP Estimation --- p.108Chapter 5.2.3 --- Repeatability Study --- p.112Chapter 5.3 --- A Study on the Beat-to-Beat BPV --- p.113Chapter 5.3.1 --- Background of the Beat-to-Beat BPV --- p.113Chapter 5.3.2 --- Analysis of the Beat-to-Beat BPV --- p.115Chapter 5.4 --- Improving the PPG Model with the Time-Varying BP --- p.120Chapter 5.4.1 --- Modification of the Model --- p.121Chapter 5.4.2 --- Simulation --- p.127Chapter 5.4.3 --- Application of the PPG Model --- p.132Chapter 5.5 --- Discussion & Conclusion --- p.134Chapter 6 --- A Novel Biometric Approach --- p.139Chapter 6.1 --- Introduction --- p.139Chapter 6.2 --- Human Verification by PPG Signal --- p.140Chapter 6.2.1 --- Experiment --- p.141Chapter 6.2.2 --- Feature Extraction --- p.142Chapter 6.2.3 --- Decision-making --- p.143Chapter 6.2.4 --- Results --- p.146Chapter 6.3 --- Discussion --- p.149Chapter 7 --- Conclusions --- p.151Chapter 7.1 --- Conclusions of Major Contributions --- p.151Chapter 7.2 --- Work to Be Done --- p.15

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

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

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810