Respiratory Monitoring
Studies in this Category
Monitoring of respiration and cardiorespiratory interactions from multichannel seismocardiography signals
This study shows that chest vibrations measured by accelerometers can accurately track breathing and heart-lung interactions, regardless of sensor placement. It introduces a new method to analyze these signals for better health monitoring.
A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals
This study provides a groundbreaking dataset combining heart and breathing signals, enabling researchers to improve non-invasive heart and lung monitoring technologies.
Accuracy of the Instantaneous Breathing and Heart Rates Estimated by Smartphone Inertial Units
This study shows that smartphones can accurately measure heart and breathing rates using built-in sensors, offering a simple and affordable way to monitor health without extra devices.
Noncontact Multipoint Vital Sign Monitoring With mmWave MIMO Radar
This study shows how radar technology can monitor heart and lung movements at multiple chest points without physical contact, offering accurate and comfortable health tracking compared to traditional methods.
Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies
This study reviews wearable and remote devices for tracking breathing, from chest belts to advanced sensors like fiber optics and radar. These technologies could help monitor respiratory health at home or in clinics, improving care for conditions like asthma and sleep apnea.
Seismocardiography for Emotion Recognition: A Study on EmoWear with Insights from DEAP
This study shows that a single wearable accelerometer on the chest can track emotions by measuring heart and breathing vibrations, offering a simpler and cheaper way to integrate emotion recognition into daily life.
Respiratory Modulation of Sternal Motion in the Context of Seismocardiography
This study shows how chest vibrations (SCG) can track breathing and heart activity using a single wearable sensor, paving the way for simpler health monitoring devices.
Toward Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals
This research shows how a wearable chest patch can estimate lung air volume using heart signals and machine learning, offering a step toward easy, continuous respiratory health monitoring.
Seismocardiography with Smartphones: No Leap from Bench to Bedside (Yet)
This study shows that while smartphones can measure heart vibrations, the technology isn’t ready for clinical use due to lack of validation and standardization compared to other methods like PPG.
Heart Rate and Respiratory Rate Monitoring Using Seismocardiography
This study shows that SCG can accurately measure heart and breathing rates, offering a non-invasive alternative to traditional methods like ECG and respiratory belts.
The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care
This study reviews how BCG and SCG technologies are being revived to monitor heart and health conditions, with potential applications in sleep and cardiovascular care. It calls for making these technologies more accessible and standardized for everyday use.
Advanced Fusion and Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals
This study shows how heart vibration signals can be used to estimate breathing rate accurately without invasive procedures, using advanced signal processing techniques like EMD and fusion methods.
Design and Development of a Portable Recording System for Simultaneous Acquisition of SCG and ECG Signals
This research developed a portable device that uses vibrations from the chest to monitor heart and breathing activity, showing promise for easier heart health tracking alongside traditional ECG tests.
Determining the Respiratory State From a Seismocardiographic Signal--A Machine Learning Approach
This study shows that chest vibrations from the heart (SCG signals) can predict breathing patterns using advanced machine learning, with neural networks being the most accurate method. This could help monitor breathing and heart health more easily and affordably.
A Machine Learning Approach to Assess the Separation of Seismocardiographic Signals by Respiration
This study shows that machine learning can classify heart vibrations based on breathing patterns, with lung volume proving to be a better grouping method than respiratory phases for reducing signal variability.
Application of Acceleration Sensors in Physiological Experiments
This study shows how accelerometers can monitor heart activity and breathing, paving the way for wearable health devices that track fitness and medical conditions more effectively.
Combined measurement of ECG, Breathing and Seismocardiograms DataBase (CEBSDB)
This dataset combines heart, breathing, and vibration signals to study how breathing affects heart rate measurements and improve vibration-based heart monitoring technologies.
Amplitude Modulation Effects in Cardiac Signals
This study shows how to better analyze heart signals by using simple techniques to reveal hidden patterns, which could improve heart monitoring methods.