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Healthy sleep is one of the prerequisites for a good human body and brain condition, including general well-being. Unfortunately, there are several sleep disorders that can negatively affect this. One of the most common is sleep apnoea, in which breathing is impaired. Studies have shown that this disorder often remains undiagnosed. To avoid this, developing a system that can be widely used in a home environment to detect apnoea and monitor the changes once therapy has been initiated is essential. The conceptualisation of such a system is the main aim of this research. After a thorough analysis of the available literature and state of the art in this area of knowledge, a concept of the system was created, which includes the following main components: data acquisition (including two parts), storage of the data, apnoea detection algorithm, user and device management, data visualisation. The modules are interchangeable, and interfaces have been defined for data transfer, most of which operate using the MQTT protocol. System diagrams and detailed component descriptions, including signal requirements and visualisation mockups, have also been developed. The system's design includes the necessary concepts for the implementation and can be realised in a prototype in the next phase.
The influence of sleep on human health is enormous. Accordingly, sleep disorders can have a negative impact on it. To avoid this, they should be identified and treated in time. For this purpose, objective (with an appropriate device) or subjective (based on perceived values) measurement methods are used for sleep analysis to understand the problem. The aim of this work is to find out whether an exchange of the two methods is possible and can provide reliable results. In accordance with this goal, a study was conducted with people aged over 65 years old (a total of 154 night-time recordings) in which both measurement methods were compared. Sleep questionnaires and electronic devices for sleep assessment placed under the mattress were applied to achieve the study aims. The obtained results indicated that the correlation between both measurement methods could be observed for sleep characteristics such as total sleep time, total time in bed and sleep efficiency. However, there are also significant differences in absolute values of the two measurement approaches for some subjects/nights, which leads us to conclude that the substitution is more likely to be considered in case of long-term monitoring where the trends are of more importance and not the absolute values for individual nights.
The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety.
Development of an expert system to overpass citizens technological barriers on smart home and living
(2023)
Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the ”Finde-Dein-SmartHome” tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.
The development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training.
MiniKueWeE-Abwärmenutzung
(2023)
Das Thema Energiewende ist derzeit so aktuell wie nie. Neben dem Umstieg von fossilen auf erneuerbare Energien gewinnt auch die Energieeffizienz auf allen Ebenen immer mehr an Bedeutung. Dies gilt besonders für viele Teile des Gebäudebereichs, wo heute eine beachtliche Energiemenge, nicht nur für die Wärmeerzeugung, sondern auch zur Raumkühlung benötigt wird (Umweltbundesamt 2020). In Anbetracht der Klimaveränderungen wird der Kühlbedarf in den nächsten Jahrzehnten zudem noch weiter ansteigen. Aus diesem Grund gibt es einen großen Bedarf an innovativen Lösungen, welche eine effiziente Raumkühlung unter möglichst geringem Energieeinsatz gewährleisten. Die vorliegende Projektarbeit untersucht einen Teilbereich einer solchen Lösung. Genaueres zum Hintergrund, den technischen Randbedingungen sowie den Zielen des Projekts, wird in den folgenden Abschnitten erläutert.
Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL).
Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM).
Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy.
Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
The architecture, engineering and construction (AEC) industry is currently in transformation. Within this transformation, digitalization has a leading function, whereby a higher level of efficiency is pursued. In order to ensure a coordinated information exchange within the digitalization, the concept of the Common Data Environment (CDE) has been developed in the last years. A CDE is a cloud based collaborative platform that is used to exchange project information and data between the different stakeholders of a construction project. The main objective of this thesis is the implementation of a suitable CDE solution for the purpose of a construction management company.
For this purpose, an evaluation of different CDE software on the market, based on the functions and usability of the different software, has been complied to identify the most suitable solution. Secondly, a concept for the setup and implementation of a CDE solution has been developed. Therefore, advice for a successful change-management has been established to ensure the good implementation of the CDE.
The first part of the thesis includes a literature review through which the current state of the information management in the AEC industry is analyzed. The advantages of a CDE for the information management are also analyzed. Therefore, a synthesis of the functions and requirements to a CDE has been complied.
The second part of the thesis links the concept of Building Information Modelling (BIM) with the CDE. The advantages of a CDE in the BIM process has been established.
The third part of the thesis treats the evaluation of CDE software. This evaluation has been complied with a scoring method. As basis of the evaluation, a requirement list has been developed in which all the required functions of a CDE are listed.
In the fourth part of the thesis, a concept for the establishment of a CDE has been developed. The developed concept is a practical application of the standards specifications to CDE. The concept has been developed with CDE expert and CDE power users. The developed concept treats overall of the structuration, the standardization, and the implementation of a CDE.
The last part treats of the change process engender by the implementation of a CDE.
Summary, this thesis provides a structure for the implementation of a CDE software and serves as framework for companies of the AEC industry to select and establish a CDE.