Refine
Year of publication
Document Type
- Conference Proceeding (69)
- Article (23)
- Part of a Book (1)
- Other Publications (1)
Keywords
- 1D-CNN (1)
- AAL (2)
- AHI (1)
- Accelerometer (3)
- Accelerometer sensor (2)
- Accelerometers (1)
- Accessibility (1)
- Activity monitoring (1)
- Algorithm (1)
- Ambient assisted living (1)
- Apnea detection (1)
- Artefaktkorrektur (1)
- Artificial intelligence models (1)
- Assisted living (1)
- Assistive systems (1)
- Atmung (1)
- Atmungssignal (1)
- Automated Artefact Separation (1)
- Automatic sleep assessment (1)
- Automotive (1)
- BCG (1)
- Ballistocardiography (3)
- Ballistokardiographie (1)
- Bewegungssignal (1)
- Bi-LSTM Model (1)
- Biomedical Engineering (1)
- Biomedical Signal Capturing (1)
- Biomedical signal processing (1)
- Biomedical time series (1)
- Biovital signal (2)
- Blockchain (1)
- Body Position (1)
- Body sensor networks (1)
- Body-movement (1)
- Breathing (1)
- Breathing rate (2)
- Butterworth filter (1)
- CNN (1)
- Cardiac activity (1)
- Cardiorespiratory Parameters (1)
- Cardiorespiratory parameters (1)
- Contactless Measurement (1)
- Contactless measurement (4)
- Contactless technologies (1)
- Data Model (1)
- Data acquisition (1)
- Data fusion (1)
- Deep Learning (1)
- Deep learning (2)
- Digital twin (1)
- Distributed ledger (1)
- Driver Drowsiness Detection (1)
- Driving (1)
- Driving Simulator (1)
- Driving safety (1)
- Driving stress (1)
- Drowsiness (1)
- Drug identification (1)
- Dynamic cluster analysis (1)
- Dynamic time warping (1)
- E-Health (1)
- ECG (5)
- ECG holter (1)
- EEG (1)
- EKG (1)
- EMG (1)
- EPQR-S (1)
- Early mobilization (1)
- Elastic domes (1)
- Electrocardiogram (1)
- Electrocardiographic signals (1)
- Electrocardiography (1)
- Electroencephalography (1)
- Exercise (1)
- Exergaming (1)
- Expert systems (1)
- FSR Sensors (1)
- FSR sensors (3)
- Force resistor sensor (1)
- Forcesensitive resistor sensors (1)
- Gamification (2)
- Generative Adversarial Networks (1)
- Hardware prototyping (1)
- Health care (1)
- Health information exchange (1)
- Health monitoring (5)
- Health monitoring systems (1)
- Health systems (1)
- Healthcare (2)
- Heart Rate (1)
- Heart rate (6)
- Heart rate estimation (1)
- Heart rate variability (1)
- Heart rate variability (HRV) (1)
- Heartbeat (1)
- Herzfrequenz (1)
- Home health (1)
- Home health systems (1)
- Impedance measurement (1)
- Internet of Things (1)
- Interoperability (1)
- J-Peak (1)
- Long-term care (4)
- Machine Learning (1)
- Machine Learning Algorithms (1)
- Machine learning (3)
- Maschinelles Lernen (2)
- Medication adherence (1)
- Mobile healthcare (1)
- Movement detection (1)
- Movement signals (1)
- Multinomial logistic regression (1)
- Müdigkeitserkennung (1)
- NFC (2)
- Non REM stage (1)
- Non-invasive (1)
- OPTICS clustering (1)
- OSA (1)
- Objective Sleep Measurement (1)
- Objective and subjective sleep measurement (1)
- Obstructive Sleep Apnea (1)
- Obstructive sleep apnea (1)
- PPG (1)
- PSG (1)
- PSQI (1)
- Personality trait (1)
- Personalized medicine (1)
- Photoplethysmography (1)
- Physiological signals (1)
- Polysomnography (PSG) (1)
- Polysomnography system (PSG) (1)
- Population ageing (1)
- Portable monitor (1)
- Precision Medicine (1)
- Precision medicine (1)
- Pressure sensor (1)
- Pressure sensors (1)
- Pulse oximeter (1)
- REM stage (1)
- RESTful API (1)
- Regression analysis (1)
- Respiration rate (2)
- Respiratory signal (1)
- Respiratory sounds (1)
- Revised eysenck personality questionnaire (1)
- Schlaf (1)
- Schlafanalyse (2)
- Schlafqualität (1)
- Schlafstadien (2)
- Seismocardiography (1)
- Sensor data (1)
- Sensor grid (1)
- Sensors (2)
- Signal processing (5)
- Sleep (4)
- Sleep Apnea (1)
- Sleep Diary (1)
- Sleep Efficiency (1)
- Sleep Monitoring (1)
- Sleep Study (1)
- Sleep apnea (1)
- Sleep apnoea (1)
- Sleep assessment (1)
- Sleep diary (3)
- Sleep efficiency (4)
- Sleep measurements (1)
- Sleep medicine (4)
- Sleep monitoring (3)
- Sleep monitoring systems (1)
- Sleep pattern (1)
- Sleep phase (1)
- Sleep quality (4)
- Sleep scoring (1)
- Sleep stage (1)
- Sleep stage classification (1)
- Sleep stages (4)
- Sleep study (14)
- Sleep/Wake states (1)
- Smart bed (2)
- Smart cushion (2)
- Smart home (1)
- Smart-care (2)
- Smart-home (1)
- Smartwatch (1)
- Stethoscope (1)
- Stress (3)
- Stress Perceived Questionnaire (PSQ) (1)
- Stress detection (2)
- Subjective sleep assessment (3)
- Survey systems (1)
- Sustainable technologies (1)
- Synthetic Data (1)
- System design (1)
- Technology acceptance (1)
- Tele monitoring (1)
- Telemedicine (2)
- Temporal feature stacking (1)
- Unobtrusive Measurement (1)
- Videoanalyse (1)
- Vital signals (2)
- Wavelet signal processing (1)
- Wearable (4)
- Wearables (2)
- Worries (1)
- Zeitreihenklassifikation (1)
- eHealth (1)
Institute
Assistive environments are entering our homes faster than ever. However, there are still various barriers to be broken. One of the crucial points is a personalization of offered services and integration of assistive technologies in common objects and therefore in a regular daily routine. Recognition of sleep patterns for the preliminary sleep study is one of the health services that could be performed in an undisturbing way. This article proposes the hardware system for the measurement of bio-vital signals necessary for initial sleep study in a non-obtrusive way. The first results confirm the potential of measurement of breathing and movement signals with the proposed system.
Autism spectrum disorders (ASD) affect a large number of children both in the Russian Federation and in Germany. Early diagnosis is key for these children, because the sooner parents notice such disorders in a child and the rehabilitation and treatment program starts, the higher the likelihood of his social adaptation. The difficulties in raising such a child lie in the complexity of his learning outside of children's groups and the complexity of his medical care. In this regard, the development of digital applications that facilitate medical care and education of such children at home is important and relevant. The purpose of the project is to improve the availability and quality of healthcare and social adaptation at home of children with ASD through the use of digital technologies.
The investigation of stress requires to distinguish between stress caused by physical activity and stress that is caused by psychosocial factors. The behaviour of the heart in response to stress and physical activity is very similar in case the set of monitored parameters is reduced to one. Currently, the differentiation remains difficult and methods which only use the heart rate are not able to differentiate between stress and physical activity, without using additional sensor data input. The approach focusses on methods which generate signals providing characteristics that are useful for detecting stress, physical activity, no activity and relaxation.
The goal of this paper pretends to show how a bed system with an embedded system with sensor is able to analyze a person’s movement, breathing and recognizing the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors.
Die Erholung unseres Körpers und Gehirns von Müdigkeit ist direkt abhängig von der Qualität des Schlafes, die aus den Ergebnissen einer Schlafstudie ermittelt werden kann. Die Klassifizierung der Schlafstadien ist der erste Schritt dieser Studie und beinhaltet die Messung von Biovitaldaten und deren weitere Verarbeitung. Das non-invasive Schlafanalyse-System basiert auf einem Hardware-Sensornetz aus 24 Drucksensoren, das die Schlafphasenerkennung ermöglicht. Die Drucksensoren sind mit einem energieeffizienten Mikrocontroller über einen systemweiten Bus mit Adressarbitrierung verbunden. Ein wesentlicher Unterschied dieses Systems im Vergleich zu anderen Ansätzen ist die innovative Art, die Sensoren unter der Matratze zu platzieren. Diese Eigenschaft erleichtert die kontinuierliche Nutzung des Systems ohne fühlbaren Einfluss auf das gewohnte Bett. Das System wurde getestet, indem Experimente durchgeführt wurden, die den Schlaf verschiedener gesunder junger Personen aufzeichneten. Die ersten Ergebnisse weisen auf das Potenzial hin, nicht nur Atemfrequenz und Körperbewegung, sondern auch Herzfrequenz zu erfassen.
The goal of the presented project is to develop the concept of home ehealth centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant diseases for a detailed study within this project because of their widespread influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services.
Fatigue and drowsiness are responsible for a significant percentage of road traffic accidents. There are several approaches to monitor the driver’s drowsiness, ranging from the driver’s steering behavior to analysis of the driver, e.g. eye tracking, blinking, yawning or electrocardiogram (ECG). This paper describes the development of a low-cost ECG sensor to derive heart rate variability (HRV) data for the drowsiness detection. The work includes the hardware and the software design. The hardware has been implemented on a printed circuit board (PCB) designed so that the board can be used as an extension shield for an Arduino. The PCB contains a double, inverted ECG channel including low-pass filtering and provides two analog outputs to the Arduino, that combined them and performs the analog-to-digital conversion. The digital ECG signal is transferred to an NVidia embedded PC where the processing takes place, including QRS-complex, heart rate and HRV detection as well as visualization features. The compact resulting sensor provides good results in the extraction of the main ECG parameters. The sensor is being used in a larger frame, where facial-recognition-based drowsiness detection is combined with ECG-based detection to improve the recognition rate under unfavorable light or occlusion conditions.
Autismus-Spektrum-Störungen (ASD) bei Kindern werden häufig zu spät diagnostiziert und die Begleitung der chronischen Krankheit gestaltet sich schwierig. Der vorgestellte Ansatz erlaubt die Behandlung der Kinder in dem bekannten häuslichen Umfeld und versucht die Beziehungen zwischen Schlaf und Verhalten herauszuarbeiten. Die gewonnenen Erkenntnisse sollen die Lebensqualität der Patienten verbessern und den Eltern Hilfestellung geben. Die notwendige infrastrukturelle Unterstützung wird durch medizinisches Fachpersonal geleistet, das auf einen web-basierten Service zurückgreifen kann, der sämtliche Prozesse (Diagnostik, Datenerfassung, -aufzeichnung und Training etc.) begleitet. Die anonymisierten Daten werden in einem Diagnosesystem zentral abgelegt und können so für zukünftige Behandlungsstrategien nutzbar sein. Die umfassende Lösung setzt auf zentrale Elemente von Smart-Homes und AAL auf.
The recovery of our body and brain from fatigue directly depends on the quality of sleep, which can be determined from the results of a sleep study. The classification of sleep stages is the first step of this study and includes the measurement of vital data and their further processing. The non-invasive sleep analysis system is based on a hardware sensor network of 24 pressure sensors providing sleep phase detection. The pressure sensors are connected to an energy-efficient microcontroller via a system-wide bus. A significant difference between this system and other approaches is the innovative way in which the sensors are placed under the mattress. This feature facilitates the continuous use of the system without any noticeable influence on the sleeping person. The system was tested by conducting experiments that recorded the sleep of various healthy young people. Results indicate the potential to capture respiratory rate and body movement.