Through the wall human heart beat detection using single channel CW radar

Single-channel continuous wave (CW) radar is widely used and has gained popularity due to its simple architecture despite its inability to measure the range and angular location of the target. Its popularity arises in the industry due to the simplicity of the required components, the low demands on the sampling rate, and their low costs. Through-the-wall life signs detection using microwave Doppler Radar is an active area of research and investigation. Most of the work in the literature focused on utilizing multi-channel frequency modulated continuous wave (FMCW), CW, and ultra-wideband (UWB) radar for their capability of range and direction of arrival (DOA) estimation. In this paper, through-the-wall single-subject and two-subject concurrent heart rate detection using single-channel 24-GHz CW radar leveraged with maximal overlap discrete wavelet transform (MODWT) is proposed. Experimental results demonstrated that the repetitive measurement of seven different subjects at a distance of 20 cm up to 100 cm through two different barriers (wood and brick wall) showed an average accuracy of heart rate extraction of 95.27% for varied distances (20–100 cm) in comparison with the Biopac ECG acquisition signal. Additionally, the MODWT method can also isolate the independent heartbeat waveforms from the two subjects’ concurrent measurements through the wall. This involved four trials with eight different subjects, achieving an accuracy of 97.04% for a fixed distance of 40 cm from the Radar without estimating the angular location of the subjects. Notably, it also superseded the performance of the direct FFT method for the single subject after 40 cm distance measurements. The proposed simpler architecture of single-channel CW radar leveraged with MODWT has several potential applications, including post-disaster search and rescue scenarios for finding the trapped, injured people under the debris, emergency evacuation, security, surveillance, and patient vital signs monitoring

Doppler radar remote sensing of respiratory function

Doppler radar remote sensing of torso kinematics can provide an indirect measure of cardiopulmonary function. Motion at the human body surface due to heart and lung activity has been successfully used to characterize such measures as respiratory rate and depth, obstructive sleep apnea, and even the identity of an individual subject. For a sedentary subject, Doppler radar can track the periodic motion of the portion of the body moving as a result of the respiratory cycle as distinct from other extraneous motions that may occur, to provide a spatial temporal displacement pattern that can be combined with a mathematical model to indirectly assess quantities such as tidal volume, and paradoxical breathing. Furthermore, it has been demonstrated that even healthy respiratory function results in distinct motion patterns between individuals that vary as a function of relative time and depth measures over the body surface during the inhalation/exhalation cycle. Potentially, the biomechanics that results in different measurements between individuals can be further exploited to recognize pathology related to lung ventilation heterogeneity and other respiratory diagnostics

NON-CONTACT AND SECURE RADAR-BASED CONTINUOUS IDENTITY AUTHENTICATION IN MULTIPLE-SUBJECT ENVIRONMENTS

Ph.D

Denoising ECG Signal using Adaptive Filter Algorithms and Cubic Spline Interpolation for Regaining Missing data points of ECG in Telecardilogy System

ABSTRACT Maintaining one's health is a fundamental human right although one billion people do not have access to quality healthcare services. Telemedicine can help medical facilities reach their previously inaccessible target community. The Telecardiology system designed and implemented in this research work is based on the use of local market electronics. In this research work we tested three algorithms named as LMS (Least Mean Square), NLMS (Normalized Mean Square), and RLS (Recursive Least Square).We have used 250 mV amplitude ECG signal from MIT-BIH database and 5mV(2 % of original ECG signal), 10 mV(4% of original ECG) 15mV (6% of original ECG),20 mV(8% of original ECG signal) and 25mV(10% of original ECG signal) of random noise and white Gaussian noise is added with ECG signal and Adaptive filter with three different algorithms have been used to reduce the noise that is added during transmission through the telemedicine system. Normalized mean square error was calculated and our MATLAB simulation results suggest that RLS performs better than other two algorithms to reduce the noise from ECG. During analog transmission of ECG signal through existing Telecommunication network some data points may be lost and we have theoretically used Cubic Spline interpolation to regain missing data points. We have taken 5000 data points of ECG Signal from MIT-BIH database. The normalized mean square error was calculated for regaining missing data points of the ECG signal and it was very less in all the conditions. Cubic Spline Interpolation function on MATLAB platform could be a good solution for regaining missing data points of original ECG signal sent through our proposed Telecardiology system but practically it may not be efficient on

Wireless Sensor Network using Particle Swarm Optimization

Abstract — Wireless sensor network (WSN) is becoming progressively important and challenging research area. A Wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical and environmental conditions and to co-operatively pass their data through the network to a main location. Wireless sensor consists of small low cost sensor nodes, having a limited transmission range and their processing, storage capabilities and energy resources are limited. The main task of such a network is to gather information from a node and transmit it to a base station for further processing.WSN has different issues such as optimal sensor deployment, node localization, base station placement, location of target nodes, energy aware clustering and data aggregation. Recently researchers around the world are applying bio-inspired optimization algorithm known as particle swarm optimization (PSO) for increasing efficiency in the WSN issues. This paper describes the use of PSO algorithm for optimal sensor deployment in WSN

Non‐contact vital signs monitoring in broiler chickens

Abstract Measuring the heartbeat and respiration of animals continuously can provide valuable information about their health status. Respiratory‐related diseases are very common in the poultry industry and unfortunately, there is no non‐contact respiratory monitoring system for measuring the breathing rate and heart rate of the broiler chicken. In this letter, the authors explored and tested the feasibility of utilizing a 24‐GHz continuous‐wave (CW) radar module for monitoring the vital signs (breathing rate and heart rate) of broiler chickens. A signal processing approach has been developed to extract vital signs of broiler chicken from Radar‐captured signals. The experiment was carried out on three different normal broiler chickens with the ages of 25–30 days, weight 1.18–1.6 kg where a 24‐GHz radar module was mounted at a distance of 0.2 m above the chest surface of the chicken, and this particular experiment was repeated for 20 times. The authors also used a reference ECG module (Biopac System) for extracting the breathing rate and heart rate of the broiler chicken and compared the accuracy of their proposed system. Experimental results demonstrated that the radar measurement closely matches the Biopac ECG acquisition module measurement and showed an accuracy of 96% for a short‐scale study

Radar-Based Non-Contact Continuous Identity Authentication

Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts in authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society