Refine
Year of publication
Document Type
- Conference Proceeding (101)
- Article (21)
- Other Publications (3)
- Part of a Book (2)
Has Fulltext
- no (127) (remove)
Keywords
- AAL (3)
- AHI (1)
- Accelerometer (2)
- Accelerometer sensor (2)
- Accelerometers (2)
- Accessibility (1)
- Activity monitoring (1)
- Algorithm (1)
- Ambient assisted living (2)
- Apnea detection (1)
- Apnoe (1)
- Artefaktkorrektur (1)
- Artificial intelligence models (1)
- Assisted living (1)
- Assistive systems (1)
- Atmung (2)
- Atmungssignal (1)
- Automated Artefact Separation (1)
- BCG (2)
- BCG signal (1)
- Ballistocardiography (2)
- Ballistocardiography (BCG) (1)
- Ballistokardiographie (1)
- Barriers (1)
- Bewegung (1)
- Bewegungssignal (1)
- Bi-LSTM Model (1)
- Bio-vital data (1)
- Biomedical Engineering (1)
- Biomedical signal processing (1)
- Biomedical time series (1)
- Biosignal analysis (1)
- Biosignal processing (1)
- Biovital signal (2)
- Blockchain (1)
- Body Position (1)
- Body sensor networks (1)
- Body-movement (1)
- Breathing (2)
- Breathing rate (2)
- Butterworth filter (1)
- CNN (1)
- Cardiorespiratory Parameters (1)
- Cardiorespiratory parameters (2)
- Contactless Measurement (1)
- Contactless measurement (3)
- Convolution (1)
- Convolutional neural network (1)
- Correlation (1)
- Data Model (1)
- Data acquisition (1)
- Data fusion (1)
- Deep Convolutional Neural Network (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 simulator (1)
- Driving stress (1)
- Drug identification (1)
- Dynamic cluster analysis (1)
- Dynamic time warping (1)
- E-Health (1)
- ECG (6)
- ECG holter (1)
- EEG (1)
- EKG (1)
- EMG (1)
- EPQR (1)
- Early mobilization (1)
- Elastic domes (1)
- Electrocardiogram (1)
- Electrocardiographic signals (1)
- Electrocardiography (3)
- Electroencephalography (1)
- Electromyography (1)
- Emotion status (1)
- Empirical mode decomposition (EMD) (1)
- Exercise (1)
- Exergaming (1)
- Expert systems (1)
- FSR Sensors (1)
- FSR sensor (1)
- FSR sensors (3)
- Force resistor sensor (1)
- Forcesensitive resistor sensors (1)
- Form factor (1)
- Gamification (2)
- Generative Adversarial Networks (1)
- Hardware prototyping (1)
- Health care (1)
- Health information exchange (1)
- Health monitoring (4)
- Health parameters (1)
- Health systems (1)
- Healthcare (2)
- Heart Rate (1)
- Heart rate (10)
- Heart rate estimation (1)
- Heart rate variability (1)
- Heartbeat (1)
- Herzfrequenz (2)
- Home health (1)
- Home health systems (1)
- Illuminance (1)
- Impedance measurement (1)
- Internet of Things (1)
- Interoperability (1)
- IoT (1)
- J-Peak (1)
- Kontaktloses Hardware-System (1)
- Long-term care (4)
- Low-pass filters (1)
- Machine Learning (1)
- Machine Learning Algorithms (1)
- Machine learning (3)
- Maschinelles Lernen (2)
- Medication adherence (1)
- Mobile App (1)
- Mobile healthcare (1)
- Monitoring (2)
- Movement detection (3)
- Movement signals (1)
- Multinomial logistic regression (2)
- Müdigkeitserkennung (1)
- NFC (2)
- Neuronal Netze (1)
- Non REM stage (1)
- Non-invasive (1)
- Non-invasive sleep study (2)
- OPTICS clustering (1)
- OSA (1)
- Objective Sleep Measurement (1)
- Obstructive Sleep Apnea (1)
- PPG (1)
- PSG (1)
- PSQ (1)
- PSQI (1)
- Palliative Care (1)
- Pattern recognition (1)
- Personalized medicine (1)
- Photoplethysmography (1)
- Physical activity (2)
- Polysomnography (PSG) (1)
- Polysomnography system (PSG) (1)
- Population ageing (1)
- Posture tracking (1)
- Precision Medicine (1)
- Precision medicine (1)
- Pressure sensor (1)
- Pressure sensors (2)
- Pulse oximeter (1)
- REM stage (1)
- RESTful API (1)
- Regression analysis (1)
- Rehabilitation (1)
- Remote Monitoring (1)
- Residual Neural Network (1)
- Respiration Rate (1)
- Respiration rate (2)
- Respiratory signal (1)
- Respiratory sounds (1)
- Schlaf (1)
- Schlafanalyse (4)
- Schlafphasen (1)
- Schlafphasenerkennung (1)
- Schlafqualität (1)
- Schlafstadien (2)
- Schlafstudie (1)
- Seismocardiography (1)
- Sensor Bed (1)
- Sensor data (1)
- Sensor grid (1)
- Sensor systems (1)
- Sensor technology (1)
- Sensors (3)
- Sensors fusion (1)
- Short time Fourier transformation (STFT) (1)
- Signal processing (5)
- Skin (1)
- Sleep (4)
- Sleep Diary (1)
- Sleep Efficiency (1)
- Sleep Monitoring (1)
- Sleep Stages (1)
- Sleep Study (1)
- Sleep apnea (3)
- Sleep apnoea (1)
- Sleep assessment (1)
- Sleep diary (2)
- Sleep efficiency (4)
- Sleep latency (1)
- Sleep medicine (7)
- Sleep monitoring (1)
- Sleep pattern (1)
- Sleep phase (1)
- Sleep positions (2)
- Sleep quality (5)
- Sleep stage (1)
- Sleep stage classification (2)
- Sleep stages (4)
- Sleep study (19)
- Sleep tracking (1)
- Sleep/Wake states (1)
- Smart bed (2)
- Smart cushion (2)
- Smart home (1)
- Smart-care (2)
- Smart-home (1)
- SpO2 (1)
- Stethoscope (1)
- Stress (7)
- Stress Perceived Questionnaire (PSQ) (1)
- Stress detection (3)
- Stress measurement (1)
- Subjective sleep assessment (3)
- Survey systems (1)
- Synthetic Data (1)
- System design (1)
- Tele monitoring (1)
- Telemedicine (2)
- Temporal feature stacking (1)
- Unobtrusive Measurement (1)
- Unobtrusive measurement (1)
- Videoanalyse (1)
- Vital signals (2)
- Wearable (4)
- Wearables (1)
- Worries (1)
- Zeitreihenklassifikation (1)
- eHealth (1)
Institute
Present demographic change and a growing population of elderly people leads to new medical needs. Meeting these with state of the art technology is as a consequence a rapidly growing market. So this work is aimed at taking modern concepts of mobile and sensor technology and putting them in a medical context. By measuring a user’s vital signs on sensors which are processed on a Android smartphone, the target system is able to determine the current health state of the user and to visualize gathered information. The system also includes a weather forecasting functionality, which alerts the user on possibly dangerous future meteorological events. All information are collected centrally and distributed to users based on their location. Further, the system can correlate the client-side measurement of vital signs with a server-side weather history. This enables personalized forecasting for each user individually. Finally, a portable and affordable application was developed that continuously monitors the health status by many vital sensors, all united on a common smartphone.
This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.
Stress and physical activities are important aspects of life of people. Body reactions on stress and on physical activities can be very similar but long-term stress leads to diseases and damages the body. Currently there is no method to differentiate easily and clearly between these two aspects in a time slot. We have confronted this problem while developing a mobile system for detection and analysis of stress. This paper presents an approach, which uses a long-term monitor with ECG/EKG capabilities and analysis of the heart rate data that is extracted from the device. The focus of the work is to find characteristics that are useful for differentiation between physical activity and stress.
A significant proportion of road traffic accidents are due to inattentiveness or fatigue at the wheel. Approaches to monitoring the driver's condition range from eye tracking and driving behavior analysis to yawn and blink detection and ECG measurement. This work describes the development of a mobile system for the measurement and processing of ECG data. The aim of the signal processing is to quantify the driver’s fatigue with the heartrate variability (HRV). The work includes the hardware and software design of the sensor. First, the development of low-noise electronics including AD conversion is described. Then the software signal processing with QRS complex detection and plotting front end is explained. The resulting sensor is compact, low-cost and provides a good signal for HRV extraction.
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.