Author: Wenjin Wang
Publisher: Academic Press
ISBN: 0128222824
Category : Computers
Languages : en
Pages : 364
Get Book Here
Book Description
Vital signs, such as heart rate and respiration rate, are useful to health monitoring because they can provide important physiological insights for medical diagnosis and well-being management. Most traditional methods for measuring vital signs require a person to wear biomedical devices, such as a capnometer, a pulse oximeter, or an electrocardiogram sensor. These contact-based technologies are inconvenient, cumbersome, and uncomfortable to use. There is a compelling need for technologies that enable contact-free, easily deployable, and long-term monitoring of vital signs for healthcare. Contactless Vital Signs Monitoring presents a systematic and in-depth review on the principles, methodologies, and opportunities of using different wavelengths of an electromagnetic spectrum to measure vital signs from the human face and body contactlessly. The volume brings together pioneering researchers active in the field to report the latest progress made, in an intensive and structured way. It also presents various healthcare applications using camera and radio frequency-based monitoring, from clinical care to home care, to sport training and automotive, such as patient/neonatal monitoring in intensive care units, general wards, emergency department triage, MR/CT cardiac and respiratory gating, sleep centers, baby/elderly care, fitness cardio training, driver monitoring in automotive settings, and more. This book will be an important educational source for biomedical researchers, AI healthcare researchers, computer vision researchers, wireless-sensing researchers, doctors/clinicians, physicians/psychologists, and medical equipment manufacturers. Includes various contactless vital signs monitoring techniques, such as optical-based, radar-based, WiFi-based, RFID-based, and acoustic-based methods. Presents a thorough introduction to the measurement principles, methodologies, healthcare applications, hardware set-ups, and systems for contactless measurement of vital signs using camera or RF sensors. Presents the opportunities for the fusion of camera and RF sensors for contactless vital signs monitoring and healthcare.
Author: Fan Yang
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
This thesis explores the contactless estimation of people's vital signs. We designed two camera-based systems and applied object detection algorithms to locate the regions of interest where vital signs are estimated. With the development of Deep Learning, Convolutional Neural Network (CNN) model has many applications in the real world nowadays. We applied the CNN based frameworks to the different types of camera based systems and improve the efficiency of the contactless vital signs estimation. In the field of medical healthcare, contactless monitoring draws a lot attention in the recent years because the wide use of different sensors. However most of the methods are still in the experimental phase and have never been used in real applications. We were interested in monitoring vital signs of patients lying in bed or sitting around the bed at a hospital. This required using sensors that have range of 2 to 5 meters. We developed a system based on the depth camera for detecting people's chest area and the radar for estimating the respiration signal. We applied a CNN based object detection method to locate the position of the subject lying in the bed covered with blanket. And the respiratory-like signal is estimated from the radar device based on the detected subject's location. We also create a manually annotated dataset containing 1,320 depth images. In each of the depth image the silhouette of the subject's upper body is annotated, as well as the class. In addition, a small subset of the depth images also labeled four keypoints for the positioning of people's chest area. This dataset is built on the data collected from the anonymous patients at the hospital which is substantial. Another problem in the field of human vital signs monitoring is that systems seldom contain the functions of monitoring multiple vital signs at the same time. Though there are few attempting to work on this problem recently, they are still all prototypes and have a lot limitations like shorter operation distance. In this application, we focused on contactless estimating subjects' temperature, breathing rate and heart rate at different distances with or without wearing the mask. We developed a system based on thermal and RGB camera and also explore the feasibility of CNN based object detection algorithms to detect the vital signs from human faces with specifically defined RoIs based on our thermal camera system. We proposed the methods to estimate respiratory rate and heart rate from the thermal videos and RGB videos. The mean absolute difference (MAE) between the estimated HR using the proposed method and the baseline HR for all subjects at different distances is 4.24 ± 2.47 beats per minute, the MAE between the estimated RR and the reference RR for all subjects at different distances is 1.55 ± 0.78 breaths per minute.
Author: Kevin K. M. Chan
Publisher:
ISBN:
Category : Patient monitoring
Languages : en
Pages : 107
Get Book Here
Book Description
Wireless sensing find many biomedical applications due to the benefit and convenience of having no contact with the patients. Low power radio waves, that are biologically safe, can monitor the patient's medical conditions such as the breathing and heart beat rate. Electro-magnetic (EM) waves extract the small movements of the chest and heart via field disturbance phenomena. The patient's breathing and heart beat modulate the radio waves for the vital signs to be monitored in real-time. Ultra-wideband (UWB) radar is an EM scattering technology that has been proven to discriminate targets with less than 30mm range resolution, which is smaller than the average size of a human heart. The UWB radar emits 200ps pulses that occupy a bandwidth from 3 to 10GHz. For linearly polarized radiation, the radar cross-section (RCS) of a moving target having complex shapes such as the thorax section of a human body causes regular fading. The scattering from a complex target rotates the radio wave vector to result in non-optimal signal reception due to polarization losses. The research focuses on the design of a circularly polarized (CP) UWB radar system for contactless vital signs monitoring of a patient. Circular polarization is introduced to address the fading RCS problem thus achieving a more robust system. The same CP UWB radar system with radio waves that penetrate through dielectric objects can be used for time-critical rescue operations in locating survivors buried under collapsed structures or in buildings engulfed in flames where visibility is hampered. Furthermore, by employing material characterization and microwave imaging techniques, it is possible to use the proposed system for other biomedical applications such as tumor localization and damaged tissue identification. The thesis is organized as follows. Firstly, the operation of UWB radar is presented and the research topic addressed. The pulse generator design suited for low pulse repetition rates is then introduced. The design of a decade bandwidth circularly polarized antenna array is described. The methodology and performance of the robust vital signs monitoring system is shown and compared with the linear polarized counterpart. A new method for material characterization using time domain RCS measurements is demonstrated. The future work is finally proposed.
Author: Xiaocong Ma
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Human vital signs are crucial parameters which reflect essential body functions and are often accessed by medical professionals at the first place during clinical diagnostics to provide immediate assistance in health status measurements. However, due to the recent COVID-19 pandemic, measurements made with direct body contact have become increasingly challenging and costly because of the spreading nature of this virus. Therefore, contactless vital sign measurements are highly desirable, and it motivates us to research and develop a new solution which is capable of performing real time heart rate (HR) detection, respiratory (RR) detection, and body temperature (BT) measurement together from a distant human subject under an ambient light environment. The thesis describes a new system framework, which utilizes the power of computer vision to collect remote video image data, processes them using signal processing and machine learning (ML) technologies simultaneously, and produces rapid updates on display. Furthermore, our validation analysis on the system has showed varied results based on different methodologies used, which enables us to apply the most suitable approach on each component for an optimized final integration. At the time of completing this thesis, we have achieved a complete system integrated with remote HR, RR estimations and BT detection, which are all fully functional in both real-time and offline. To further refine the performance on HR estimation, we selected the extreme gradient boost model through a number of ML models we tested, as it not only gives the lowest root mean square error of 8.2 but also produces stable and robust output.
Author: Mehrdad Nosrati
Publisher:
ISBN: 9781392799413
Category : Biosensors
Languages : en
Pages : 215
Get Book Here
Book Description
Author: Laura M. Roa Romero
Publisher: Springer Science & Business Media
ISBN: 3319008463
Category : Technology & Engineering
Languages : en
Pages : 1978
Get Book Here
Book Description
The general theme of MEDICON 2013 is "Research and Development of Technology for Sustainable Healthcare". This decade is being characterized by the appearance and use of emergent technologies under development. This situation has produced a tremendous impact on Medicine and Biology from which it is expected an unparalleled evolution in these disciplines towards novel concept and practices. The consequence will be a significant improvement in health care and well-fare, i.e. the shift from a reactive medicine to a preventive medicine. This shift implies that the citizen will play an important role in the healthcare delivery process, what requires a comprehensive and personalized assistance. In this context, society will meet emerging media, incorporated to all objects, capable of providing a seamless, adaptive, anticipatory, unobtrusive and pervasive assistance. The challenge will be to remove current barriers related to the lack of knowledge required to produce new opportunities for all the society, while new paradigms are created for this inclusive society to be socially and economically sustainable, and respectful with the environment. In this way, these proceedings focus on the convergence of biomedical engineering topics ranging from formalized theory through experimental science and technological development to practical clinical applications.
Author: Jong-Ryul Yang
Publisher: Mdpi AG
ISBN: 9783036517667
Category : Technology & Engineering
Languages : en
Pages : 142
Get Book Here
Book Description
Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data.
Author: Kian Davoudi Rad
Publisher:
ISBN:
Category :
Languages : en
Pages : 124
Get Book Here
Book Description
For remote registration of vital signs, non-invasiveness is attractive. The possibility of acute loss of consciousness threatens the lives of employees such as soldiers, fire fighters, police officers, and law enforcement personnel, who perform duties at hazardous or remote conditions. The brain cells die within three minutes after occurrence of hypoxia in cerebrospinal fluid. Consequently, acute occurrence of cardiac or pulmonary system failure reduces an employee's likelihood of surviving due to the lack of ability for communication and the lack of ability for emergency calls. In the case of severe injury, a medic's response time becomes a crucial parameter for increasing a person's likelihood of survival. Continuous monitoring of vital signs therefore assists medics and employees by reducing response time to severe accidents. For detection of vital signs; electrocardiography, capnometry and pulse oximetry are being widely used as the golden standard for extraction of vital signs such as heart rate, respiration rate and blood oxygenation. But, these sensors require motionless attachment to specific areas of the body. The fact that employees are constantly in motion and sometimes covered by protection shields introduces difficulty concerning the continuous obtaining of vital signs. In this dissertation, we studied feasibility of replacing Electrocardiography by Photoplethysmography on mechanically flexible sensors. Three major studies were performed. First, we developed an algorithm for the detection of respiration rate from Photoplethysmography. During this study, a fast respond CO2 sensor was also modulated to detect respiration rate, and both methods were compared to respiration effort transducer. Second, we assessed the feasibility of fabricating PPG sensors on plastic polymers. During this study, we designed and integrated a novel PPG device, using inkjet-printing technology. At last, we developed a computationally inexpensive algorithm for the extraction of heart rate variability (HRV) from the morphology of PPG. Results were compared to HRV from commercial ECG and the performance of the device was evaluated respectively.
Author: Mohamed Abd El Ghany
Publisher:
ISBN: 9783330072107
Category :
Languages : en
Pages : 108
Get Book Here
Book Description
Author: Keyu Pan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Contactless detection of human vital sign using radar sensors appears to be a promising technology which integrates communication, biomedicine, computer science etc. The radar-based vital sign detection has been actively investigated in the past decade. Due to the advantages such as wide bandwidth, high resolution, small and portable size etc., ultra-wideband (UWB) radar has received a great deal of attention in the health care field. In this thesis, an X4 series UWB radar developed by Xethru Company is adopted to detect human breathing signals through the radar echo reflected by the chest wall movement caused by breath and heartbeat. The emphasis is placed on the estimation of breathing and heart rate based on several signal processing algorithms. Firstly, the research trend of vital sign detection using radar technology is reviewed, based on which the advantages of contactless detection and UWB radar-based technology are highlighted. Then theoretical basis and core algorithms of radar signals detection are presented. Meanwhile, the detection system based on Xethru UWB radar is introduced. Next, several preprocessing methods including SVD-based clutter and noise removal algorithms, the largest variance-based target detection method, and the autocorrelation-based breathing-like signal identification method are investigated, to extract the significant component containing the vital signs from the received raw radar echo signal. Then the thesis investigates four time-frequency analysis algorithms (fast Fourier transform + band-pass filter (FFT+BPF), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) and compare their performances in estimating breathing rate (BR) and heart rate (HR) in different application scenarios. A python-based vital signs detection system is designed to implement the above-mentioned preprocessing and BR and HR estimation algorithms, based on which a large number of single target experiments are undertaken to evaluate the four estimation algorithms. Specifically, the single target experiments are divided into simple setup and challenging setup. In the simple setup, testees face to radar and keep normal breathing in an almost stationary posture, while in the challenging setup, the testee is allowed to do more actions, such as site sitting, changing the breathing frequency, deep hold the breathing. It is shown that the FFT+BPF algorithm gives the highest accuracy and the fastest calculation speed under the simple setup, while in a challenging setup, the VMD algorithm has the highest accuracy and the widest applicability. Finally, double targets breathing signal detection at different distances to the radar are undertaken, aiming to observe whether the breathing signals of two targets will interfere with each other. We found that when two objects are not located at the same distance to the radar, the object closer to the radar will not affect the breath detection of the object far from the radar. When the two targets are located at the same distance, the 'Shading effect' appears in the two breathing signals and only VMD algorithm can separate the breathing signals of the targets.
