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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.
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.
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.
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.
This textbook provides a comprehensive, yet practically orientated overview of classic and novel sports cardiology topics, based on current evidence, guidelines, recommendations and expert experience. Numerous publications have provided guidance to these issues, but it has become increasingly difficult for both students and doctors to obtain a thorough, but practicable overview for optimal clinical care of athletes and patients. This book is intended as an educational work, filling the large gaps that are still present in the current educational guidelines for medical students and cardiology trainees. Textbook of Sports and Exercise Cardiology differs from other sports cardiology books by focusing on clear, practical recommendations based on the latest evidence, primarily targeting those who seek professional background information and education that can easily be transferred into everyday care.
This book is a truly comprehensive, timely, and very much needed treatise on the conceptualization of analysis, and design of contactless & multimodal sensor-based human activities, behavior understanding & intervention. From an interaction design perspective, the book provides views and methods that allow for more safe, trustworthy, efficient, and more natural interaction with technology that will be embedded in our daily living environments. The chapters in this book cover sufficient grounds and depth in related challenges and advances in sensing, signal processing, computer vision, and mathematical modeling. It covers multi-domain applications, including surveillance and elderly care that will be an asset to entry-level and practicing engineers and scientists.(See inside for the reviews from top experts)
With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.
Photoplethysmography: Technology, Signal Analysis, and Applications is the first comprehensive volume on the theory, principles, and technology (sensors and electronics) of photoplethysmography (PPG). It provides a detailed description of the current state-of-the-art technologies/optical components enabling the extreme miniaturization of such sensors, as well as comprehensive coverage of PPG signal analysis techniques including machine learning and artificial intelligence. The book also outlines the huge range of PPG applications in healthcare, with a strong focus on the contribution of PPG in wearable sensors and PPG for cardiovascular assessment. Presents the underlying principles and technology surrounding PPG Includes applications for healthcare and wellbeing Focuses on PPG in wearable sensors and devices Presents advanced signal analysis techniques Includes cutting-edge research, applications and future directions
Human Factors and Wearable Technologies Proceedings of the 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022), July 24–28, 2022, New York, USA
This book provides a comprehensive overview of the key technologies and applications related to new cameras that have brought 3D data acquisition to the mass market. It covers both the theoretical principles behind the acquisition devices and the practical implementation aspects of the computer vision algorithms needed for the various applications. Real data examples are used in order to show the performances of the various algorithms. The performance and limitations of the depth camera technology are explored, along with an extensive review of the most effective methods for addressing challenges in common applications. Applications covered in specific detail include scene segmentation, 3D scene reconstruction, human pose estimation and tracking and gesture recognition. This book offers students, practitioners and researchers the tools necessary to explore the potential uses of depth data in light of the expanding number of devices available for sale. It explores the impact of these devices on the rapidly growing field of depth-based computer vision.