Institut für Angewandte Forschung - IAF
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In this paper, Hurwitz polynomials, i.e., real polynomials whose roots are located in the open left half of the complex plane, and their associated Hurwitz matrices are considered. New formulae for the principal minors of Hurwitz matrices are presented which lead to: (i) a new criterion for deciding whether a polynomial is Hurwitz, (ii) an inequality of a type of Oppenheim's inequality for the Hurwitz matrices up to order 6, and (iii) a necessary and sufficient condition for the Hadamard square root of Hurwitz polynomials of degree five to be Hurwitz.
Driving behaviour is a critical factor in accidents today. Physiological factors have a significant impact on driving behaviour. A potential solution lies in vehicle services that benefit from sensing environmental conditions to improve road safety, such as collision avoidance routines in driver assistance systems. Stress, assessed subjectively or physiologically, influences decision making and behaviour, with implications for individuals and the economy. In this paper we present a novel approach to formulate a risk index by combining data from subjective self-reports and objective physiological measures (in particular heart rate). The model identifies stress tendencies in driving behaviour by monitoring behavioural and physiological markers. We present our evaluation results and explore potential ways to implement the model in vehicle systems and its implications for improving road safety. We discuss potential enhancements to improve driving safety and enable timely responses in situations with an increased risk of accidents due to stress or drowsiness.
Development of a digital CBT-I tool for user-friendly treatment and observation of insomnia patients
(2024)
Sufficient and regular sleep is essential for people’s physical and psychological health and a good life quality. Insomnia is one of the most frequent sleep disorders worldwide. Patients with a significant leak of sleep can also have a higher risk for depression. The main goal of this research was to create the concept of a software solution based on “Cognitive behavior therapy for insomnia” or short CBT-I, to support patients to enhance their sleep quality. But also, to make the monitoring of multiple patients more efficient and accessible, for the therapist’s side. Methods that can be quantified and are subsequently easy to automate were transferred, form CBT-I. One of the most essential components of this concept is the sleep diary, being used for the collection of the data for further processing. The PSQI questionnaire with an evaluation function was also included because it is an important tool for the therapist side. To guarantee access to the data and results for both sides, the data are stored on an online database. An internal test with 3 users confirmed a good user experience and has shown that the implementation of a user-friendly CBT-I based software solution for the simultaneous use is realizable and can provide benefits for patients and therapists. Like better monitoring and the automation of numerous proven CBT-I methods, e.g. sleep restriction or the transfer of CBT-I knowledge, making a considerable part of the process more efficient. This increase in efficiency can enable therapists to treat more patients simultaneously, while maintaining the same level of quality.
The classification of sleep and wake states is of paramount importance in the context of sleep disorders. In order to detect and monitor disorders such as obstructive sleep apnea (OSA), it is essential to obtain the total sleep time (TST) so as to assess the severity of the patient’s sleep apnea. With the advent of new technologies for detecting events associated with sleep disorders, it is not always straightforward to calculate the sleep/wakefulness state. Consequently, this work presents the development of a deep learning model (a variant of U-Net) for the detection of sleep/wakefulness states. For this purpose, an engineering approach using Keras Tuner and the use of three signals with minimal processing was employed. The three signals, oxygen saturation (SpO2), heart rate (HR) and abdominal respiratory effort (AbdRes), were selected to ensure both patient comfort during signal collection and the possibility of using portable monitors. The models were trained and tested on data from polysomnography studies, namely the Sleep Heart Health Study (SHHS) and the Multiethnic Study of Atherosclerosis (MESA). The best performing model achieved results with 88% binary precision, 88% recall, 89% precision, 89% f1-score and Cohen’s Kappa of 0.74 for the SHHS test set. The model obtained 82% binary accuracy, 82% recall, 84% precision, 82% f1-score and 0.62 Cohen’s kappa for the MESA data set.
Deployment of Artificial Intelligence Models for Sleep Apnea Recognition in the Sleep Laboratory
(2024)
There are a large number of scientific publications that focus on the development and evaluation of artificial intelligence (AI) models for the detection of various pathologies in the field of sleep medicine. However, most of these publications do not show the process or methodology to be followed for the final deployment of these models in a complete diagnostic system (in terms of software and hardware). This is a major drawback when translating from the development or research environment to the real clinical setting. This work focuses on a methodology for deploying an AI model for sleep apnea detection with the end user in mind: the clinician. For the deployment, the transmission of data between the device, the cloud platform and the machine learning server, as well as the protocols used, were considered. In addition, the storage and visualization of the data has been taken into account so that it can be analyzed accurately by experts.
Non-invasive sleep monitoring holds significant promise for enhancing healthcare by offering insights into sleep quality and patterns. In this context, accurate detection of body position is crucial, as it provides essential information for diagnosing and understanding the causes of various sleep disorders, including sleep apnea. The aim of this work is to develop an efficient system for sleep position detection using a minimal number of FSR (Force Sensitive Resistor) sensors and advanced machine learning techniques. A hardware setup was developed incorporating 3 FSR sensors, on-board signal processing for frequency boundary filtering and gain adjustment, an ADC (Analog-to-digital converter), and a computing unit for data processing. The collected data was then cleaned and structured before applying various machine learning models, including Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and XGBoost. An experiment with 15 subjects in 4 different sleeping positions was conducted to evaluate the system. The SVC demonstrated notable performance with a test accuracy of 64%. Analysis of the results identified areas for future improvement, including better differentiation between similar positions. The study highlights the feasibility of using FSR sensors and machine learning for effective sleep position detection. However, further research is needed to improve accuracy and explore more advanced techniques. Future efforts will aim to integrate this approach into a comprehensive, unobtrusive sleep monitoring system, contributing to better healthcare services.
Sleep is a crucial aspect of human well-being, with significant implications for overall health and quality of life. In response to the growing concern over sleep-related issues and the need for innovative solutions, this paper presents ‘Sleep Sheep’, an innovative system designed to monitor sleep and promote healthy sleep habits. The motivation behind Sleep Sheep stems from recognizing the vital role sleep plays in our daily lives. Inadequate sleep has been associated with various health problems, including cognitive impairments, mood disorders, and compromised immune function. Thus, addressing sleep-related concerns has become a pressing priority. To achieve its objectives, Sleep Sheep utilizes a smartphone application monitored by a doctor, to collect and analyze comprehensive sleep data, including sleep duration, sleep stages, and sleep disturbances. The collected data is then processed using several algorithms to provide results, which demonstrate its potential to impact sleep quality and overall well-being. By providing users with personalized sleep reports and actionable recommendations, Sleep Sheep makes individuals aware that they should adopt healthier sleep practices. These reports and recommendations, in turn, can lead to improved sleep duration, enhanced sleep efficiency, and a better overall sleep experience. By fostering awareness about the importance of sleep and providing individuals with the tools for being monitored by their doctors and improving their sleep, Sleep Sheep has the potential to make a substantial impact on public health. Ultimately, this innovative system aims to contribute to the well-being and quality of life of individuals by encouraging healthy sleep habits and optimizing sleep outcomes.
The importance of sleep in the life of a human being to function in nowadays society is known from a large number of studies. A standard method for sleep analysis is polysomnography (PSG), which uses multiple sensors to measure and analyze multiple signals, allowing precise and detailed sleep analysis. Nonetheless, the cost of using PSG technologies is high in terms of complexity, personnel and time. To overcome these shortcomings, alternative solutions can be used to reduce costs and increase comfort for patients. The objective of this work is to design and develop a prototype of the software component of the sleep analysis system, taking into account the aspects of data flow, data storage and user interface in addition to data processing. The software components implemented and developed in the Morpheus System and described in this article comprise a usable platform capable of assisting custom research implementations in IoT.
Paying attention helps us learn, advance in our careers, and build successful relationships, but when it’s compromised, achievement of any kind becomes far more challenging. Causes of not paying attention can range from common factors like sleep deprivation, stress, or a mood disorder to health difficulties such as ADHD, OCD, or a thyroid problem that affects concentrating. This work extracts paying attention and not paying attention behavior patterns in the context of learning. In early work, our study identified attention and distraction behaviors using gathered video recordings of online classes. The work found ten paying attention behaviors and six distracted behavior patterns. In this paper, we use computer vision techniques to extract features related to these behaviors. These features are distance between hand and face, pitch yaw roll, eye-to-camera distance, hand-to-camera distance, iris direction, gaze tracking, mouth aspect ratio, eye aspect ratio, distance between face and frame side, and facial landmark configuration. This research also applied three types of machine learning—logistic regression, decision trees, and random forest—and the accuracy rates were 79%, 86%, and 89%, respectively. This result is better than relying only on two extracted features in our previous work.
The heart of the project is the early and cost-effective diagnosis of impulse control disorders in children and adolescents. The methodology is based on the automatic analysis of speech and sleep patterns, which is being carried out in cooperation with Colombian and German partners. The group has set itself three project goals. In the first step, the synchronization of ongoing project work will be carried out so that, on the one hand, available results can be incorporated into this project and, on the other hand, cooperation results can be taken into account in ongoing work. Parallel to this, the second project objective is to set up competence groups that, as specialist groups, are familiar with the regional characteristics and help to record the current situation. The first two objectives are supported in particular by workshops and the exchange of researchers. In this way, the partners’ methodology is made accessible to both groups, which significantly promotes the analysis of research topics and the approach. Finally, in the third project objective, practice-oriented and target group-oriented results based on validated case studies will be provided so that the jointly developed methodology can be created. This contribution provides an overview of the activities.
