As the world population ages, the healthcare system is facing new challenges in treating more patients at a lower cost than today. One trend in addressing this problem is to increase the opportunities of in-home care. To achieve this there is a need for safe and cost-effective monitoring systems. Biosignal PI is an ongoing open-source project created to develop a flexible and affordable platform for development of stand-alone devices able to measure and process physiological signals. This master thesis project, performed at the department of Medical Sensors, Signals and System at the School of Technology and Health, aimed at further develop the Biosignal PI software by constructing a new flexible software framework architecture that could be used for measurement and processing of different types of biosignals. The project also aimed at implementing features for Heart Rate Variability(HRV) Analysis in the Biosignal PI software as well as developing a graphical user interface(GUI) for the Raspberry PI hardware module PiFace Control and Display. The project developed a new flexible abstract software framework for the Biosignal PI. The new framework was constructed to abstract all hardware specifics into smaller interchangeable modules, with the idea of the modules being independent in handling their specific task making it possible to make changes in the Biosignal PI software without having to rewrite all of the core. The new developed Biosignal PI software framework was implemented into the existing hardware setup consisting of an Raspberry PI, a small and affordable single-board computer, connected to ADAS1000, a low power analog front end capable of recording an Electrocardiography(ECG). To control the Biosignal PI software two different GUIs were implemented. One GUI extending the original software GUI with the added feature of making it able to perform HRV-Analysis on the Raspberry PI. This GUI requires a mouse and computer screen to function. To be able to control the Biosignal PI without mouse the project also created a GUI for the PiFace Control and Display. The PiFace GUI enables the user to collect and store ECG signals without the need of an big computer screen, increasing the mobility of the Biosignal PI device.   To help with the development process and also to make the project more compliant with the Medical Device Directive a couple of development tools were implemented such as a CMake build system, integrating the project with the Googletest testing framework for automated testing and the implementation of the document generator software Doxygen to be able to create an Software Documentation.    The Biosignal PI software developed in this thesis is available through Github at https://github.com/biosignalpi/Version-A1-Rapsberry-PI / Allt eftersom världens befolkning åldras, ställs sjukvården inför nya utmaningar i att behandla fler patienter till en lägre kostnad än idag. En trend för att lösa detta problem är att utöka möjligheterna till vård i hemmet.För att kunna göra detta finns det ett ökande behov av säkra och kostnadseffektiva patientövervakningssystem. Biosignal PI är ett pågående projekt med öppen källkod som skapats för att utveckla en flexibel och prisvärd plattform för utveckling av fristående enheter som kan mäta och bearbeta olika fysiologiska signaler. Detta examensarbete genomfördes vid institutionen för medicinska sensorer, signaler och system vid Skolan för Teknik och Hälsa. Projektet syftade till att vidareutveckla den befintliga mjukvaran för Biosignal PI genom att skapa ett nytt flexibelt mjukvaruramverk som kan användas för mätning och bearbetning av olika typer av biosignaler.Projektet syftade också till att utvidga mjukvaran och lägga till funktioner för att kunna genomföra hjärtfrekvensvariabilitets(HRV) analys i Biosignal PIs mjukvara, samt att utveckla ett grafiskt användargränssnitt(GUI) för hårdvarumodulen PiFace Control and Display. Projektet har utvecklat ett nytt flexibelt mjukvaruramverk för Biosignal PI. Det nya ramverket konstruerades för att abstrahera alla hårdvaruspecifika delar in i mindre utbytbara moduler, med tanken att modulerna ska vara oberoende i hur de hanterar sin specifika uppgift. På så sätt ska det vara möjligt att göra ändringar i Biosignal PIs programvara utan att behöva skriva om hela mjukvaran.Det nyutvecklade Biosignal PI ramverket implementerades i det befintliga hårdvaru systemet, som består av en Raspberry PI, liten och prisvärd enkortsdator, ansluten till ADAS1000, en analog hårdvarumodul med möjlighet att registrera ett elektrokardiografi(EKG/ECG). För att kontrollera Biosignal PI programmet har två olika grafiska användargränssnitt skapats.Det ena gränssnitt är en utvidgning av original programvaran med tillagd funktionalitet för att kunna göra HRV-Analys på Raspberry PI, detta gränssnitt kräver dock mus och dataskärm för att kunna användas.För att kunna styra Biosignal PI utan mus och skärm skapades det även ett gränssnitt för PiFace Control and Display. PiFace gränssnittet gör det möjligt för användaren att samla in och lagra EKG-signaler utan att behöva en stor datorskärm, på så sätt kan man öka Biosignal PI systemets mobilitet. För att underlätta utvecklingsprocessen, samt göra projektet mer förenligt med det medicintekniska regelverket, har ett par utvecklingsverktyg integrerats till Biosignal PI projektet såsom CMake för kontroll av kompileringsprocessen, test ramverket Googletest för automatiserad testning samt integrering med dokumentations generatorn Doxygen för att kunna skapa en dokumentation av mjukvaran.

Die langfristigen Folgen von psychischer Fehlbeanspruchung stellen ein beträchtliches Problem unserer modernen Gesellschaft dar. Zur Identifizierung derartiger Fehlbelastungen während der Mensch-Maschine-Interaktion (MMI) kann die objektive, kontinuierliche Messung der psychischen Beanspruchung einen wesentlichen Beitrag leisten. Neueste Entwicklungen in der Sensortechnologie und der algorithmischen Methodenentwicklung auf Basis von KI liefern die Grundlagen zu ihrer messtechnischen Bestimmung. Vorarbeiten zur Entwicklung einer Methode zur neuronalen Beanspruchungsdiagnostik sind bereits erfolgt (Radüntz, 2017). Eine praxisrelevante Nutzung dieser Ergebnisse ist erfolgsversprechend, wenn die Methode mit Wearables kombiniert werden kann. Gleichzeitig sind die Evaluation und bedingungsbezogene Reliabilitätsprüfung der entwickelten Methode zur neuronalen Beanspruchungsdiagnostik in realitätsnahen Umgebungen erforderlich. Im Rahmen von experimentellen Untersuchungen der Gebrauchstauglichkeit von kommerziellen EEG-Registrierungssystemen für den mobilen Feldeinsatz wird die darauf basierende Systemauswahl für die MMI-Praxis getroffen. Die Untersuchungen zur Validierung der kontinuierlichen Methode zur Beanspruchungsdetektion erfolgt am Beispiel des Fluglotsenarbeitsplatzes beim simulierten „Arrival Management“. / The long-term negative consequences of inappropriate mental workload on employee health constitute a serious problem for a digitalized society. Continuous, objective assessment of mental workload can provide an essential contribution to the identification of such improper load. Recent improvements in sensor technology and algorithmic methods for biosignal processing are the basis for the quantitative determination of mental workload. Neuronal workload measurement has the advantage that workload registration is located directly there where human information processing takes place, namely the brain. Preliminary studies for the development of a method for neuronal workload registration by use of the electroencephalogram (EEG) have already been carried out [Rad16, Rad17]. For the field use of these findings, the mental workload assess- ment on the basis of the EEG must be evaluated and its reliability examined with respect to several conditions in realistic environments. A further essential require-ment is that the method can be combined with the innovative technologies of gel free EEG registration and wireless signal transmission. Hence, the presented papers include two investigations. Main subject of the first investigation are experimental studies on the usability of commercially-oriented EEG systems for mobile field use and system selection for the future work. Main subject of the second investigation is the evaluation of the continuous method for neuronal mental workload registration in the field. Thereby, a challenging application was used, namely the arrival management of aircraft. The simulation of the air traffic control environment allows the realisation of realistic conditions with different levels of task load. Furthermore, the work is well contextualized in a domain which is very sensible to human-factors research.

This work is dedicated to dynamic time warping in biosignal processing, especially it´s application for ECG signals. On the beginning the theoretical notes about cardiography are summarized. Then, the DTW analysis follows along with conditions and demands assessments for it’s successful application. Next, several variants and application possibilities are described. The practical part covers the design of this method, the outputs comprehension, settings optimization and realization of methods related with DTW

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden. / (Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features.

Acute mental stress is an everyday phenomenon that has evidently intensified over the past decades and poses significant health risks. Conventional methods for stress assessment are not suitable for everyday use. They are suitable only for clinical and laboratory assessment because they require full attention, limit the freedom of movement (sensors, cables), often require trained personnel or special equipment, and thus are cost-intensive. This work investigates camera-based photoplethysmography (cbPPG), a non-contact technique for the monitoring of cardiovascular vital signs, as an alternative for the assessment of acute mental stress that is suitable for everyday use. As a non-contact technique cbPPG is considered susceptible to artifacts. To overcome limitations of existing cbPPG methods, this work covers essential developments for the robust extraction of non-contact vital signs in addition to the assessment of acute mental stress. An experimental study was designed and conducted with 65 healthy participants to gain a database for cbPPG including synchronized reference measurements (e.g. electrocardiography, skin conductance, salivary cortisol concentration). The experimental study resulted in the „Dresden Multimodal Biosignal Dataset for the Mannheim Multi-component Stress Test“ (DMBD). In addition, the „Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database“ (BP4D+) was utilized. For robust extraction of non-contact vital signs measured with cbPPG, a novel method for the extraction of cbPPG signals was developed: O3C. O3C optimizes the combination of the color channels of RGB cameras with an evaluation metric in a specialized, systematic grid search. Several investigations on properties of the novel method revealed that the grid search always identified a global optimum. O3C was independent of different skin tones and the choice of evaluation metric. Temporal normalization of the RGB color channels improved the transferability of O3C between datasets (DMBD, BP4D+). At the example of breath rate measurement, it was shown that the method behind O3C is transferable from pulse rate to other vital signs. In addition, a novel method for automatic, reference-free identification of erroneous measurements was developed on the basis of signal quality indexes (SQIs). The developments on robust extraction of non-contact vital signs contribute to the fundamentals of cardiovascular monitoring that is suitable for everyday use. Among other aspects, this forms the basis for non-contact assessment of acute mental stress with cbPPG. In the experimental study (DMBD), conventional reference methods showed distinct changes in psychometric variables, chemical biomarkers, and contact-based vital signs during acute mental stress. The results are widely in line with existing literature and indicated successful activation of the hypothalamic-pituitary-adrenal axis (HPA axis) as well as sympathetic activation of the autonomic nervous system. A special characteristic of this investigation on stress assessment resides in the large variety of synchronized reference parameters, which allows a side-by-side comparison of the effectiveness of different measurement techniques. To assess the physiological reaction to acute mental stress with non-contact technique, ten vital signs derived with cbPPG were analyzed. The cbPPG vital signs registered positive chronotropy, peripheral vasoconstriction, and altered respiration in accordance with reference measurements. Thus, they also successfully indicated sympathetic activation of the autonomic nervous system. In a machine learning approach, the cbPPG vital signs were effective in detecting the immediate stress response with a fairly high temporal resolution of 30 s. These investigations are unique in terms of their extent and the possibility to adduce diverse synchronized reference measurements for comparison. They provide valuable insights into capabilities and effectiveness of cbPPG for non-contact assessment of acute mental stress. The findings of this work pave the way for robust non-contact monitoring with cbPPG. At the example of acute mental stress, a method for physiological assessment of the human state that is suitable for everyday use has been presented. This provides new opportunities to make use of the great potential that cbPPG offers for numerous everyday applications (e.g. telemedical video consultations, adaptive human-machine interfaces).:1 Introduction .. 1.1 Relevance .. 1.2 Scope .. 1.3 Outline .. 1.4 Delineation 2 Physiological Fundamentals .. 2.1 Stress and Strain .. .. 2.1.1 Historical Development .. .. 2.1.2 Definition .. 2.2 Endocrine System .. 2.3 Autonomic Nervous System .. 2.4 Cardiovascular System .. .. 2.4.1 Heart .. .. 2.4.2 Vascular System .. .. 2.4.3 Facial Vasculature .. 2.5 Skin 3 Methods to Assess the Human Response to Acute Mental Stress .. 3.1 Clinical and Laboratory Procedures .. .. 3.1.1 Stress Induction .. .. 3.1.2 Stress Response Assessment .. 3.2 Biomedical Engineering Techniques .. .. 3.2.1 Conventional Techniques .. .. .. 3.2.1.1 Electrocardiography .. .. .. 3.2.1.2 Photoplethysmography .. .. .. 3.2.1.3 Blood Pressure Measurement .. .. .. 3.2.1.4 Electrodermal Activity .. .. .. 3.2.1.5 Vital Signs of Conventional Techniques .. .. 3.2.2 Non-contact Techniques .. .. .. 3.2.2.1 Overview .. .. .. 3.2.2.2 Comparison .. 3.3 Summary 4 Camera-based Photoplethysmography .. 4.1 Functional Principle .. 4.2 Measurement Technology .. 4.3 Pulse Rate Measurement .. 4.4 Algorithms for Signal Extraction .. .. 4.4.1 Image Processing .. .. 4.4.2 Channel Combination .. .. 4.4.3 Signal Processing .. .. 4.4.4 Excursus: A Note on Deep Learning .. .. 4.4.5 Summary .. 4.5 Application to Stress Assessment 5 Study Design .. 5.1 Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database .. 5.2 Dresden Multimodal Biosignal Dataset for the Mannheim Multicomponent Stress Test .. .. 5.2.1 Protocol .. .. 5.2.2 Setup .. .. 5.2.3 Annotations .. .. 5.2.4 Cohort Summary 6 Investigations on Robust Extraction of Non-contact Vital Signs .. 6.1 Color Space Transformations .. 6.2 Novel Method for the Optimization of Color Channel Combinations .. 6.3 Impact of Skin Tone on the Optimal Color Channel Combination .. 6.4 Impact of Normalization on the Optimal Color Channel Combination .. 6.5 Impact of Evaluation Metric on the Optimal Color Channel Combination .. 6.6 Optimal Color Channel Combination for Breath Rate Measurement .. 6.7 Signal Quality Index Filtering .. 6.8 Summary 7 Investigations on the Assessment of Acute Mental Stress .. 7.1 Examination of Reference Parameters .. 7.2 Examination of Camera-based Vital Signs .. 7.3 Prediction from Camera-based Vital Signs .. 7.4 Summary 8 Conclusion .. 8.1 Summary .. 8.2 Outlook References Appendix .. A Schematic Structure of the Autonomic Nervous System .. B Other Conventional Techniques for Biosignal Acquisition .. C Recording and Synchronization of the Dresden Multimodal Biosignal Dataset for the Mannheim Multicomponent Stress Test .. D Definition of Regions of Interest From Facial Landmarks .. E Definition of Color Space Transformations .. F Extended Results of Camera-based Pulse Rate Measurement With Different Color Spaces and Regions of Interest .. G Level-Set Regions of Interest in the Experimental Study .. H Relative Accuracy Differences Across the Hemispherical Surface Grid for Multiple Settings .. I Descriptive Statistics for the Reference Vital Signs of the Experimental Study .. J Insignificant Reference Vital Signs of the Experimental Study .. K Statistics for the Binary Logistic Regression with Forward Selection .. .. K.1 Omnibus Tests of Model Coefficients .. .. K.2 Model Summary .. .. K.3 Hosmer and Lemeshow Test .. .. K.4 Classification Table .. .. K.5 Equation Variables / Akuter mentaler Stress ist ein alltägliches Phänomen, dass sich im Laufe der vergangenen Jahrzehnte nachweislich intensiviert hat und ein Risiko für die Gesundheit darstellt. Herkömmliche Methoden zur Stressbewertung sind nicht alltagstauglich. Sie eignen sich nur für Klinik und Labor, da sie volle Aufmerksamkeit erfordern, Bewegungsfreiheit einschränken (Sensoren, Kabel), zumeist Fachpersonal oder Spezialausrüstung voraussetzen und entsprechend kostenintensiv sind. Diese Arbeit beschäftigt sich mit der kamerabasierten Photoplethysmographie (cbPPG), einer kontaktlosen Technik zum Monitoring kardiovaskulärer Vitalparameter, als alltagstaugliche Alternative zur Bewertung der physiologischen Reaktion auf akuten mentalen Stress. Als kontaktlose Technologie gilt cbPPG allerdings als artefaktanfällig. Um Limitationen bestehender Methoden zu überwinden, umfasst diese Arbeit neben der Stressbewertung mit cbPPG essenzielle Weiterentwicklungen zur robusten Extraktion kontaktloser Vitalparameter. Um eine Datenbasis für cbPPG mit zahlreichen Referenzmessverfahren (z. B. Elektrokardiografie, Hautleitfähigkeit, Speichelkortisolkonzentration) zu schaffen, wurde eine Experimentalstudie mit 65 gesunden Probanden aufgesetzt. Daraus resultierte das „Dresden Multimodal Biosignal Dataset for the Mannheim Multi-component Stress Test“ (DMBD). Zusätzlich fand die „Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database“ (BP4D+) Anwendung. Für die robuste Extraktion von Vitalparametern mit cbPPG wurde eine neuartige Methodik zur Signalextraktion entwickelt: O3C. O3C optimiert die Kombination der Farbkanäle einer RGB-Kamera in einer spezialisierten, systematischen Rastersuche anhand einer Evaluationsmetrik. Die Untersuchung zentraler Eigenschaften von O3C zeigte, dass stets ein globales Optimum der Rastersuche existiert und die neue Methode robust gegenüber verschiedenen Hauttönen und Evaluationsmetriken ist. Zeitliche Normalisierung der RGB-Farbkanäle verbesserte die Übertragbarkeit von O3C zwischen verschiedenen Datensätzen (DMBD, BP4D+). Am Beispiel der Atemratenmessung wurde gezeigt, dass die Methodik von O3C auf andere Vitalparameter übertragbar ist. Darüber hinaus wurde eine neue Methode zur referenzfreien Identifikation fehlerhafter Messungen mittels Signalqualitätsindizes (SQIs) entwickelt. Die Entwicklungen zur robusten Extraktion von Vitalparametern leisten einen grundlegenden Beitrag für das alltagstaugliche kardiovaskuläre Monitoring mit cbPPG. Damit schaffen sie unter anderem die Voraussetzung für die kontaktlose Stressbewertung mit cbPPG. Die Referenzmessverfahren der Experimentalstudie (DMBD) zeigten bei akutem mentalem Stress deutliche Veränderungen psychometrischer Variablen, chemischer Biomarker und kontaktbasiert erfasster Vitalparameter. Die Ergebnisse stehen in weitreichender Übereinstimmung mit bisheriger Literatur und wiesen die erfolgreiche Aktivierung der Hypothalamus-Hypophysen-Nebennierenrinden-Achse und die sympathische Aktivierung des autonomen Nervensystems aus. Eine Besonderheit dieser Untersuchung zur Stressbewertung liegt in der Vielfalt synchronisierter Referenzparameter, mit der sich die Effektivität verschiedener Referenzmessverfahren direkt gegenüberstellen lässt. Für die kontaktlose Bewertung der physiologischen Reaktion auf akuten mentalen Stress wurden zehn cbPPG Vitalparameter analysiert. Die cbPPG Vitalparameter erfassten positive Chronotropie, periphere Vasokonstriktion und veränderte Atmung, und zeigten damit ebenfalls die sympathische Aktivierung des autonomen Nervensystems erfolgreich an. Die cbPPG Vitalparameter eigneten sich darüber hinaus zur zuverlässigen automatisierten Detektion der unmittelbaren Stressreaktion mit einer hohen zeitlichen Auflösung von 30 s. Die Untersuchungen sind einzigartig in ihrem Umfang und der Möglichkeit, diverse Referenzmessverfahren zum Vergleich heranzuziehen. Sie liefern damit wertvolle Erkenntnisse über Möglichkeiten und Leistungsfähigkeit von cbPPG zur kontaktlosen Stressbewertung. Die Ergebnisse dieser Arbeit ebnen den Weg für ein robustes kontaktloses Monitoring mittels cbPPG. Am Beispiel akuten mentalen Stresses wurde eine Methode zur alltagstauglichen Bewertung physiologischer Zustände aufgezeigt. Damit eröffnen sich neue Möglichkeiten, das große Potenzial von cbPPG für zahlreiche Anwendungsfälle (z. B. adaptive Mensch-Maschine-Schnittstellen, telemedizinische Videokonsultationen) alltagstauglich zu erschließen.:1 Introduction .. 1.1 Relevance .. 1.2 Scope .. 1.3 Outline .. 1.4 Delineation 2 Physiological Fundamentals .. 2.1 Stress and Strain .. .. 2.1.1 Historical Development .. .. 2.1.2 Definition .. 2.2 Endocrine System .. 2.3 Autonomic Nervous System .. 2.4 Cardiovascular System .. .. 2.4.1 Heart .. .. 2.4.2 Vascular System .. .. 2.4.3 Facial Vasculature .. 2.5 Skin 3 Methods to Assess the Human Response to Acute Mental Stress .. 3.1 Clinical and Laboratory Procedures .. .. 3.1.1 Stress Induction .. .. 3.1.2 Stress Response Assessment .. 3.2 Biomedical Engineering Techniques .. .. 3.2.1 Conventional Techniques .. .. .. 3.2.1.1 Electrocardiography .. .. .. 3.2.1.2 Photoplethysmography .. .. .. 3.2.1.3 Blood Pressure Measurement .. .. .. 3.2.1.4 Electrodermal Activity .. .. .. 3.2.1.5 Vital Signs of Conventional Techniques .. .. 3.2.2 Non-contact Techniques .. .. .. 3.2.2.1 Overview .. .. .. 3.2.2.2 Comparison .. 3.3 Summary 4 Camera-based Photoplethysmography .. 4.1 Functional Principle .. 4.2 Measurement Technology .. 4.3 Pulse Rate Measurement .. 4.4 Algorithms for Signal Extraction .. .. 4.4.1 Image Processing .. .. 4.4.2 Channel Combination .. .. 4.4.3 Signal Processing .. .. 4.4.4 Excursus: A Note on Deep Learning .. .. 4.4.5 Summary .. 4.5 Application to Stress Assessment 5 Study Design .. 5.1 Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database .. 5.2 Dresden Multimodal Biosignal Dataset for the Mannheim Multicomponent Stress Test .. .. 5.2.1 Protocol .. .. 5.2.2 Setup .. .. 5.2.3 Annotations .. .. 5.2.4 Cohort Summary 6 Investigations on Robust Extraction of Non-contact Vital Signs .. 6.1 Color Space Transformations .. 6.2 Novel Method for the Optimization of Color Channel Combinations .. 6.3 Impact of Skin Tone on the Optimal Color Channel Combination .. 6.4 Impact of Normalization on the Optimal Color Channel Combination .. 6.5 Impact of Evaluation Metric on the Optimal Color Channel Combination .. 6.6 Optimal Color Channel Combination for Breath Rate Measurement .. 6.7 Signal Quality Index Filtering .. 6.8 Summary 7 Investigations on the Assessment of Acute Mental Stress .. 7.1 Examination of Reference Parameters .. 7.2 Examination of Camera-based Vital Signs .. 7.3 Prediction from Camera-based Vital Signs .. 7.4 Summary 8 Conclusion .. 8.1 Summary .. 8.2 Outlook References Appendix .. A Schematic Structure of the Autonomic Nervous System .. B Other Conventional Techniques for Biosignal Acquisition .. C Recording and Synchronization of the Dresden Multimodal Biosignal Dataset for the Mannheim Multicomponent Stress Test .. D Definition of Regions of Interest From Facial Landmarks .. E Definition of Color Space Transformations .. F Extended Results of Camera-based Pulse Rate Measurement With Different Color Spaces and Regions of Interest .. G Level-Set Regions of Interest in the Experimental Study .. H Relative Accuracy Differences Across the Hemispherical Surface Grid for Multiple Settings .. I Descriptive Statistics for the Reference Vital Signs of the Experimental Study .. J Insignificant Reference Vital Signs of the Experimental Study .. K Statistics for the Binary Logistic Regression with Forward Selection .. .. K.1 Omnibus Tests of Model Coefficients .. .. K.2 Model Summary .. .. K.3 Hosmer and Lemeshow Test .. .. K.4 Classification Table .. .. K.5 Equation Variables

This thesis deals with the problem with cluster analysis and biosignal classification options. The principle of cluster analysis, methods for calculating distances between objects and the standard process in the implementation of clustering are described in the first part. For biosignals processing,it is necessary to get familiar with the primary parameters of these signals in the following sections of thesis, process biosignals and methods for recording of action potentials described. Based on studying different clustering methods is presented a program with the applied method kmedoid in the next section of this thesis. The steps of this program are described in detail and in the end of thesis functionality is tested on a database of signals ÚBMI.

Traditional fabrication methods used to manufacture biosensors for physiological, therapeutics, or health monitoring purposes are complex and rely on costly materials, which has hindered their adoption as single-use medical devices. The development of a new kind of wearable and implantable electronics relying on inexpensive materials for their manufacturing will pave the way towards the ubiquitous adoption of sticker-like health tracking devices.<div>One of growing and most promising applications for biosensors is the continuous health monitoring using mechanically soft, stretchable sensors. While these healthcare devices showed an excellent compatibility with human tissues, they still need highly trained personnel to perform multi-step, prolonged fabrication for several functioning layers of the device. In this dissertation, I propose low-cost, scalable, simple, and rapid manufacturing techniques to fabricate multifunctional epidermal and implantable sensors to monitor a range of biosignals including heart, muscle, or eye activity to characterizing of biofuids such as sweat. I have also used these devices as an implant to provide heat therapy for muscle regeneration and optical stimulation of neurons using optogenetics. These devices have also combined with those of triboelectric<br>nanogenerators to realize self-powered sensors for monitoring imperceptible mechanical biosignals such as respiratory and pulse rate.</div><div>Food health and safety has also emerged as another important frontier to develop biosensors and improve the human health and quality of life. The recent progresses on detecting microbial activity inside foods or their packages rely on development of highly functional materials. The existing materials for fabrication of food sensors, however,<br>are often costly and toxic for human health or the environment. In this dissertation, I proposed biocompatible food sensors using protein/PCL microfibers to reinforce the protein microfibrous structure in humid conditions and exploit their excellent hygroscopic properties to sense biogenic gas, as an indicator for early detection of food spoilage. Finally, my battery-free food sensors are capable of monitoring food safety with no need of extra measurement devices. Collectively, this dissertation proposes cost-effective solutions to solve human health issues, enabled by developing low-cost, functional materials and exploiting simple fabrication techniques.<br></div>