Single-Tone Doppler Radar System for Human Respiratory Monitoring

Radio Detection and Ranging (Radar) technology have become a great interest in various fields, including medical monitoring. Continuous Wave (CW) radar is one of commonly used technique to detect Doppler effect from a single moving target. Human respiratory can be identified by the periodic chest wall movement, which is potentially detected by using Doppler radar shift. This thesis proposes a Doppler radar system for human vital sign respiratory. A method for processing the Doppler radar output to obtain respiration information is required for a better accurate result in human respiratory rate. This thesis is to develop a method to extract respiratory information from Doppler radar output signal. The simulation is performed to investigate the ability of the proposed method in detecting the human respiration parameter such as respiration rate and respiration amplitude. In this thesis, the Single-Tone Doppler radar operating at 10 GHz is studied and is proposed for detecting human respiration. The experimental investigation is performed by computer simulation and CW radar module of HB100. The results are expected to be accurate and are capable of extracting the human respiration parameters. This thesis is expected to provide a post-processing method contribution in developing Doppler radar for non-contacting measurement devices for human respiratory. The proposed post-processing method is expected to provide a contribution in developing Doppler radar for non-contacting measurement devices for human respiratory

Single-Tone Doppler Radar System for Human Respiratory Monitoring

Human respiration activities can be identified from the chest wall movement. In developing a non-contacting sensor for human respiration, the chest wall movement can be detected as a Doppler shift. Therefore, the Doppler radar is potential to be implemented for the non-contacting sensor previously mention. In this paper, the Single-Tone Doppler radar which operates at 10 GHz has been studied and proposed for detecting human respiration. The simulation experimental is performed for investigating the capability of the proposed method in detecting the human respiration parameter such as respiration rate and respiration amplitude. The results show that the proposed method is capable to extract the human respiration parameter

Novel Methods for Weak Physiological Parameters Monitoring.

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

NON-CONTACT TECHNIQUES FOR HUMAN VITAL SIGN DETECTION AND GAIT ANALYSIS

Human vital signs including respiratory rate, heart rate, oxygen saturation, blood pressure, and body temperature are important physiological parameters that are used to track and monitor human health condition. Another important biological parameter of human health is human gait. Human vital sign detection and gait investigations have been attracted many scientists and practitioners in various fields such as sport medicine, geriatric medicine, bio-mechanic and bio-medical engineering and has many biological and medical applications such as diagnosis of health issues and abnormalities, elderly care and health monitoring, athlete performance analysis, and treatment of joint problems. Thoroughly tracking and understanding the normal motion of human limb joints can help to accurately monitor human subjects or patients over time to provide early flags of possible complications in order to aid in a proper diagnosis and development of future comprehensive treatment plans. With the spread of COVID-19 around the world, it has been getting more important than ever to employ technology that enables us to detect human vital signs in a non-contact way and helps protect both patients and healthcare providers from potentially life-threatening viruses, and have the potential to also provide a convenient way to monitor people health condition, remotely. A popular technique to extract biological parameters from a distance is to use cameras. Radar systems are another attractive solution for non-contact human vital signs monitoring and gait investigation that track and monitor these biological parameters without invading people privacy. The goal of this research is to develop non-contact methods that is capable of extracting human vital sign parameters and gait features accurately. To do that, in this work, optical systems including cameras and proper filters have been developed to extract human respiratory rate, heart rate, and oxygen saturation. Feasibility of blood pressure extraction using the developed optical technique has been investigated, too. Moreover, a wideband and low-cost radar system has been implemented to detect single or multiple human subject’s respiration and heart rate in dark or from behind the wall. The performance of the implemented radar system has been enhanced and it has been utilized for non-contact human gait analysis. Along with the hardware, advanced signal processing schemes have been enhanced and applied to the data collected using the aforementioned radar system. The data processing algorithms have been extended for multi-subject scenarios with high accuracy for both human vital sign detection and gait analysis. In addition, different configurations of this and high-performance radar system including mono-static and MIMO have been designed and implemented with great success. Many sets of exhaustive experiments have been conducted using different human subjects and various situations and accurate reference sensors have been used to validate the performance of the developed systems and algorithms

Requirements and metrics for location and tracking for ambient assisted living

Location and tracking services and technologies are becoming fundamental components for supporting healthcare solutions. They facilitate patients’ tracking and monitoring processes and also allow for better and long-term daily activity recognition. Various location and tracking services have been developed, over the last years, to provide real time localization for different applications. However, most of these services are not designed particularly to comply with all the requirements of Ambient Assisted Living (AAL) and, as a result, they reduce the viability of adopting AAL services as an alternative for continuous healthcare services. In this paper we set out the general requirements for location and tracking services for AAL. The requirements are extracted from a typical scenario of AAL. From the scenario, we define the requirements and also we identify a set of metrics to be used as evaluation criteria. If the identified requirements and metrics are adopted widely, potential location and tracking services will fit the real needs of AAL, and thus will increase the accessibility to AAL services by a larger sector of people. Moreover, in the paper, we evaluate two of the existing location techniques through the use of the proposed metrics. The aim is to asses to which level these solutions fulfill the identified requirements.This work was supported by the FEDER program through the COMPETE and the Portuguese Science and Technology Foundation (FCT), within the context of the AAL4ALL (COMPETE 13852) and FCOMP-01-FEDER-0124-022674 projects

Detecting Vital Signs with Wearable Wireless Sensors

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented

Computer Vision and Sensor Fusion for Autonomous Vehicles

Cars, particularly manually-driven cars, are one of the most commonly used modes of transportation today. However, millions of people are either killed or left with disabilities annually due to road traffic accidents caused by human error or sensor failures. Despite that, a lot of people seem reluctant to look into alternatives to manually driven vehicle transportation. This is understandable as driving cars has been the trustworthy mode of transportation for many years, and it is widely used in everyday life around the world. However, technological advances in the fields of machine learning and cyber-physical systems contributed to the emergence of nearly or fully autonomous vehicles, or driverless cars, as a true viable alternative for the current human-controlled driving mode. The technology still has a long way to go, mainly because the advances in vision and depth measurement sensors such as LIDARs can not achieve the levels of safety needed to make fully autonomous cars. Progress on this front is being made every day, and it seems inevitable that they will be readily available in the near future. Our team aims to further investigate the application of Computer Vision and sensor fusion to achieve independent self-driving without external guides. To accomplish this, we combine a depth camera with a LiDAR to provide better coverage of the surroundings and allow more accurate detection and thus accurate avoidance of obstacles. We are mounting the vision system on a model driverless car and using the vision data to guide the car control system. A computer vision algorithm will be run by the NVIDIA Jetson Nano to determine what course of action the car should take. The final prototype should be capable of driving at a reasonable speed without colliding with any objects and making decisions such as braking or turning when necessary

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown