Refine
Year of publication
Document Type
- Conference Proceeding (103) (remove)
Keywords
- AAL (1)
- AHI (1)
- Accelerometer (3)
- Accelerometer sensor (1)
- Accelerometers (1)
- Accessibility (1)
- Activity monitoring (1)
- Algorithm (1)
- Ambient assisted living (1)
- Apnoe (1)
- Assisted living (1)
- Assistive systems (1)
- Atmung (1)
- Atmungssignal (1)
- Automated Artefact Separation (1)
- Automotive (1)
- BCG (2)
- Ballistocardiography (3)
- Ballistocardiography (BCG) (1)
- Ballistokardiographie (1)
- Barriers (1)
- Bewegung (1)
- Bewegungssignal (1)
- Bio-vital data (1)
- Biomedical Engineering (1)
- Biomedical Signal Capturing (1)
- Biomedical time series (1)
- Biosignal analysis (1)
- Biosignal processing (1)
- Biovital signal (2)
- Body sensor networks (1)
- Body-movement (1)
- Breathing (2)
- Breathing rate (2)
- Butterworth filter (1)
- CNN (1)
- Cardiorespiratory Parameters (1)
- Cardiorespiratory parameters (1)
- Contactless Measurement (1)
- Contactless measurement (4)
- Convolution (1)
- Convolutional neural network (1)
- Correlation (1)
- Data acquisition (1)
- Data fusion (1)
- Deep Convolutional Neural Network (1)
- Deep Learning (1)
- Deep learning (2)
- Digital twin (1)
- Driver Drowsiness Detection (1)
- Driving (1)
- Driving Simulator (1)
- Driving simulator (1)
- Drowsiness (1)
- Drug identification (1)
- Dynamic cluster analysis (1)
- Dynamic time warping (1)
- ECG (6)
- ECG holter (1)
- EKG (1)
- EMG (1)
- EPQR (1)
- Early mobilization (1)
- Elastic domes (1)
- Electrocardiogram (1)
- Electrocardiography (3)
- Electroencephalography (1)
- Electromyography (1)
- Emotion status (1)
- Empirical mode decomposition (EMD) (1)
- Exercise (1)
- Exergaming (1)
- Expert systems (1)
- FSR sensor (1)
- FSR sensors (3)
- Force resistor sensor (1)
- Forcesensitive resistor sensors (1)
- Form factor (1)
- Gamification (2)
- Generative Adversarial Networks (1)
- Health monitoring (3)
- Health parameters (1)
- Health systems (1)
- Healthcare (2)
- Heart Rate (1)
- Heart rate (7)
- Heartbeat (1)
- Herzfrequenz (2)
- Home health (1)
- Home health systems (1)
- Illuminance (1)
- Impedance measurement (1)
- Internet of Things (1)
- Interoperability (1)
- Kontaktloses Hardware-System (1)
- Long-term care (4)
- Low-pass filters (1)
- Machine Learning (1)
- Machine learning (3)
- Maschinelles Lernen (2)
- Mobile App (1)
- Monitoring (1)
- Movement detection (2)
- Movement signals (1)
- Multinomial logistic regression (1)
- NFC (1)
- Neuronal Netze (1)
- Non REM stage (1)
- Non-invasive (1)
- Non-invasive sleep study (1)
- OPTICS clustering (1)
- OSA (1)
- Objective Sleep Measurement (1)
- Obstructive Sleep Apnea (1)
- PPG (1)
- PSG (1)
- PSQ (1)
- PSQI (1)
- Palliative Care (1)
- Personalized medicine (1)
- Photoplethysmography (1)
- Physical activity (2)
- Polysomnography (PSG) (1)
- Polysomnography system (PSG) (1)
- Population ageing (1)
- Precision Medicine (1)
- Precision medicine (1)
- Pressure sensor (1)
- Pressure sensors (2)
- Pulse oximeter (1)
- REM stage (1)
- RESTful API (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 (1)
- Schlafphasen (1)
- Schlafphasenerkennung (1)
- Schlafqualität (1)
- Schlafstadien (2)
- Schlafstudie (1)
- Seismocardiography (1)
- Sensor Bed (1)
- Sensor grid (1)
- Sensor technology (1)
- Sensors (2)
- Sensors fusion (1)
- Short time Fourier transformation (STFT) (1)
- Signal processing (4)
- Skin (1)
- Sleep (4)
- Sleep Diary (1)
- Sleep Efficiency (1)
- Sleep Monitoring (1)
- Sleep Stages (1)
- Sleep Study (1)
- Sleep apnea (2)
- 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 (1)
- Sleep quality (4)
- Sleep stage (1)
- Sleep stage classification (1)
- Sleep stages (2)
- Sleep study (15)
- 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)
- Temporal feature stacking (1)
- Unobtrusive Measurement (1)
- Videoanalyse (1)
- Vital signals (2)
- Wearable (4)
- Wearables (1)
- Worries (1)
- Zeitreihenklassifikation (1)
- eHealth (1)
Institute
The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (long-term electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic Time Warping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.
To evaluate the quality of a person's sleep it is essential to identify the sleep stages and their durations. Currently, the gold standard in terms of sleep analysis is overnight polysomnography (PSG), during which several techniques like EEG (eletroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), SpO2 (blood oxygen saturation) and for example respiratory airflow and respiratory effort are recorded. These expensive and complex procedures, applied in sleep laboratories, are invasive and unfamiliar for the subjects and it is a reason why it might have an impact on the recorded data. These are the main reasons why low-cost home diagnostic systems are likely to be advantageous. Their aim is to reach a larger population by reducing the number of parameters recorded. Nowadays, many wearable devices promise to measure sleep quality using only the ECG and body-movement signals. This work presents an android application developed in order to proof the accuracy of an algorithm published in the sleep literature. The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%.
Stress is recognized as a factor of predominant disease and in the future the costs for treatment will increase. The presented approach tries to detect stress in a very basic and easy to implement way, so that the cost for the device and effort to wear it remain low. The user should benefit from the fact that the system offers an easy interface reporting the status of his body in real time. In parallel, the system provides interfaces to pass the obtained data forward for further processing and (professional) analyses, in case the user agrees. The system is designed to be used in every day’s activities and it is not restricted to laboratory use or environments. The implementation of the enhanced prototype shows that the detection of stress and the reporting can be managed using correlation plots and automatic pattern recognition even on a very light-weighted microcontroller platform.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.