A5019899495- EEG and Brain-Computer Interfaces
- Artificial Intelligence in Healthcare
- Gaze Tracking and Assistive Technology
- Sleep and Work-Related Fatigue
- Advanced Chemical Sensor Technologies
- Emotion and Mood Recognition
- Parkinson's Disease Mechanisms and Treatments
- Diabetes Management and Research
- Human Pose and Action Recognition
- Anomaly Detection Techniques and Applications
- Semantic Web and Ontologies
- Cerebral Palsy and Movement Disorders
- Sleep and Wakefulness Research
- Time Series Analysis and Forecasting
- Food Allergy and Anaphylaxis Research
- Obstructive Sleep Apnea Research
- Context-Aware Activity Recognition Systems
- Biomedical Text Mining and Ontologies
- Learning Styles and Cognitive Differences
- Advanced Sensor and Energy Harvesting Materials
- Spectroscopy Techniques in Biomedical and Chemical Research
- Electrochemical sensors and biosensors
- Non-Invasive Vital Sign Monitoring
- Balance, Gait, and Falls Prevention
- Human-Automation Interaction and Safety
University of Lübeck
2022-2025
Institute for Medical Informatics and Biostatistics
2023
University of Siegen
2018
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our stage classification method addressing the following two challenges: first is data imbalance problem, i.e., highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings seek balance sizes different stages. second challenge difficulty identifying stages,...
The operation of a motor vehicle under the influence alcohol poses significant risk to safety driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used understand movements behavior eyes while driving. In our study, we smart glasses collect EOG data from nine participants who driving simulator. Their level alcoholic intoxication was simulated by drunk vision goggles at three different levels inebration (0, 1, 2, 3‰ blood content). We machine learning...
Recent advancements in hardware technology have spurred a surge the popularity and ubiquity of wearable sensors, opening up new applications within medical domain. This proliferation has resulted notable increase availability Time Series (TS) data characterizing behavioral or physiological information from patient, leading to initiatives toward leveraging machine learning analysis techniques. Nonetheless, complexity time required for collecting remain significant hurdles, limiting dataset...
The perception of hunger and satiety is great importance to maintaining a healthy body weight avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due malnutrition. There are number disease patterns, characterized by loss this perception. To our best knowledge, cannot be classified using non-invasive measurements. Aiming develop an objective classification system, paper presents multimodal sensory system associated signal processing pattern recognition methods for...
Sleep is an important research area in nutritional medicine that plays a crucial role human physical and mental health restoration. It can influence diet, metabolism, hormone regulation, which affect overall well-being. As essential tool the sleep study, stage classification provides parsing of architecture comprehensive understanding patterns to identify disorders facilitate formulation targeted interventions. However, class imbalance issue typically salient datasets, severely affects...
To drive safely, the driver must be aware of surroundings, pay attention to road traffic, and ready adapt new circumstances. Most studies on driving safety focus detecting anomalies in behavior monitoring cognitive capabilities drivers. In our study, we proposed a classifier for basic activities car, based similar approach that could applied recognition daily life, is, using electrooculographic (EOG) signals one-dimensional convolutional neural network (1D CNN). Our achieved an accuracy 80%...
Abstract Continuous glucose monitoring (CGM) has transformed the care of diabetes mellitus patients. It is increasingly used to support nutritional management in various pathophysiologic conditions, including obesity and migraine, where avoiding postprandial hyperglycemia critical prevent hyperinsulinemia low levels after meal intake. However, current CGM devices have significant limitations, such as invasiveness, availability, high cost, limited shelf life, which must be overcome for...
Abstract Introduction Parkinson's disease (PD) causes significant impairment due to both motor and non-motor symptoms, which severely impact patients' health-related quality of life (HRQoL) increase caregiver burden. Given the rising prevalence PD in an aging population, particularly Germany, need for innovative resource-efficient healthcare approaches is paramount. The complexity symptoms necessity individualised, multidisciplinary digital health technology-based care are widely...
Emotion recognition is a increasingly popular topic because of its potential applications in the field affective learning. It allows development systems able to adapt themselves users' emotional state improve learner's experience and In this paper, we introduce new biomedical multi-sensor platform for realtime acquisition physiological data comprising Temperature, Electroencephalography (EEG), Electroocculography (EOG), Galvanic Skin Response (GSR), Heart Rate Blood Oxygen Saturation. We...
To drive safely, the driver must be aware of surroundings, pay attention to road traffic, and ready adapt new circumstances. Most studies on driving safety focus detecting anomalies in behavior monitoring cognitive capabilities drivers. In our study, we proposed a classifier for basic activities car, based similar approach that could applied recognition daily life, is, using electrooculographic (EOG) signals one-dimensional convolutional neural network (1D CNN). Our achieved an accuracy 80%...
Wearable Human Activity Recognition (HAR) is an important field of research in smart assistive technologies. Collecting the data needed to train reliable HAR classifiers complex and expensive. As a way mitigate scarcity, Time Series Data Augmentation (TSDA) techniques have emerged as promising approach for generating synthetic data. TSDA not trivial image augmentation has been relatively less investigated. In this paper, comparative study various state-of-the-art applied context wearable...
Cardiovascular diseases (CVDs) are chronic associated with a high risk of mortality and morbidity. Early detection CVD is crucial to initiating timely interventions, such as appropriate counseling medication, which can effectively manage the condition improve patient outcomes. This study introduces an innovative ontology-based model for diagnosis CVD, aimed at improving decision support systems in healthcare. We developed database inspired by ontology principles, tailored efficient...
Abstract Parkinson’s disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence scarce or inconclusive. Therefore, we examined the walking features executive functioning in patients with using state-of-the-art machine learning approaches. A dataset of 103 geriatric Parkinson inpatients, who performed four conditions varying difficulty levels depending on single task additional demands, was analyzed. Walking...
The recognition of physiological reactions with the help machine learning methods already plays a major role in many research areas, but is still little represented field food hypersensitivity recognition. present work addresses question how can be detected by analysing sensor data explainable algorithms. In first step, Empatica E4 wristband, wearable device that easily integrated into everyday life, collects raw on various patterns, and algorithms are implemented to extract variety features...
Cardiovascular diseases (CVD) are chronic associated with a high risk of mortality
 and morbidity. Early detection CVD is crucial to initiating timely interventions, such as appro- 
 priate counseling medication, which can effectively manage the condition improve patient
 outcomes. Preventive measures should be implemented at general public level, promoting a
 healthy lifestyle, individual that is, in people moderate CVD
 or patients already diagnosed by addressing...