Refine
Year of publication
Document Type
- Conference Proceeding (93)
- Article (28)
- Part of a Book (2)
- Other Publications (1)
- Report (1)
Language
- English (125) (remove)
Keywords
- 1D-CNN (1)
- AAL (3)
- AHI (1)
- Accelerometer (3)
- Accelerometer sensor (2)
- Accelerometers (2)
- Accessibility (1)
- Activity monitoring (1)
- Algorithm (1)
- Ambient assisted living (2)
- Apnea detection (1)
- Artificial intelligence models (1)
- Assisted living (1)
- Assistive systems (1)
- Automated Artefact Separation (1)
- Automatic sleep assessment (1)
- Automotive (1)
- BCG (1)
- BCG signal (1)
- Ballistocardiography (4)
- Ballistocardiography (BCG) (1)
- Barriers (1)
- Bi-LSTM Model (1)
- Bio-vital data (1)
- Biomedical Engineering (1)
- Biomedical Signal Capturing (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)
- Cardiac activity (1)
- Cardiorespiratory Parameters (1)
- Cardiorespiratory parameters (2)
- Contactless Measurement (1)
- Contactless measurement (4)
- Contactless technologies (1)
- 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 (3)
- Digital twin (1)
- Distributed ledger (1)
- Driver Drowsiness Detection (1)
- Driving (1)
- Driving Simulator (1)
- Driving safety (1)
- Driving simulator (1)
- Driving stress (1)
- Drowsiness (1)
- Drug identification (1)
- Dynamic cluster analysis (1)
- Dynamic time warping (1)
- E-Health (1)
- ECG (7)
- ECG holter (1)
- EKG (1)
- EMG (1)
- EPQR (1)
- EPQR-S (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)
- Freistellungssemesterbericht (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 parameters (1)
- Health systems (1)
- Healthcare (2)
- Heart Rate (1)
- Heart rate (11)
- Heart rate estimation (1)
- Heart rate variability (1)
- Heart rate variability (HRV) (1)
- Heartbeat (1)
- Home health (1)
- Home health systems (2)
- Illuminance (1)
- Impedance measurement (1)
- Internet of Things (1)
- Interoperability (1)
- IoT (1)
- J-Peak (1)
- Long-term care (4)
- Low-pass filters (1)
- Machine Learning (1)
- Machine Learning Algorithms (1)
- Machine learning (3)
- Medication adherence (1)
- Mobile App (1)
- Mobile healthcare (1)
- Monitoring (2)
- Movement detection (3)
- Movement signals (1)
- Multinomial logistic regression (2)
- NFC (2)
- Non REM stage (1)
- Non-invasive (1)
- Non-invasive sleep study (2)
- 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)
- PSQ (1)
- PSQI (1)
- Palliative Care (1)
- Pattern recognition (1)
- Personality trait (1)
- Personalized medicine (1)
- Photoplethysmography (1)
- Physical activity (2)
- Physiological signals (1)
- Polysomnography (PSG) (1)
- Polysomnography system (PSG) (1)
- Population ageing (1)
- Portable monitor (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 (3)
- Respiratory signal (1)
- Respiratory sounds (1)
- Revised eysenck personality questionnaire (1)
- Seismocardiography (1)
- Sensor Bed (1)
- Sensor data (1)
- Sensor grid (1)
- Sensor systems (1)
- Sensor technology (1)
- Sensors (4)
- Sensors fusion (1)
- Short time Fourier transformation (STFT) (1)
- Signal processing (5)
- Skin (1)
- Sleep (5)
- Sleep Apnea (1)
- 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 (3)
- Sleep efficiency (4)
- Sleep latency (1)
- Sleep measurements (1)
- Sleep medicine (7)
- Sleep monitoring (4)
- Sleep monitoring systems (1)
- Sleep pattern (1)
- Sleep phase (1)
- Sleep positions (2)
- Sleep quality (5)
- Sleep scoring (1)
- Sleep stage (1)
- Sleep stage classification (2)
- Sleep stages (5)
- 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)
- Smartwatch (1)
- SpO2 (1)
- Stethoscope (1)
- Stress (8)
- Stress Perceived Questionnaire (PSQ) (1)
- Stress detection (3)
- Stress measurement (1)
- 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)
- Unobtrusive measurement (1)
- Vital signals (2)
- Wavelet signal processing (1)
- Wearable (4)
- Wearables (2)
- Worries (1)
- eHealth (1)
Institute
Ballistocardiography (BCG) can be used to monitor heart rate activity. Besides, the accelerometer should have high sensitivity and minimal internal noise; a low-cost approach was taken into consideration. Several measurements have been executed to determine the optimal positioning of a sensor under the mattress to obtain a signal strong enough for further analysis. A prototype for an unobtrusive accelerometer-based measurement system has been developed and tested in a conventional bed without any specific extras. The influence of the human sleep position for the output accelerometer data was tested. The obtained results indicate the potential to capture BCG signals using accelerometers. The measurement system can detect heart rate in an unobtrusive form in the home environment.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.