Experimental validation of a quasi-realtime human respiration detection method via UWB radar

In this paper, we propose a quasi-realtime human respiration detection method via UWB radar system in through-wall or similar condition. With respect to the previous proposed automatic detection method, the new proposed method assures competitive performance in the human respiration motion detection and effective noise/clutter rejection, which have been proved by experimental results in actual scenario. This new method has also been implemented in a UWB through-wall life-detection radar prototype, and its time consuming is about 2 s, which can satisfy the practical requirement of quasi-realtime for through-wall sequential vital sign detection. Therefore, it can be an alternative for through-obstacles static human detection in antiterrorism or rescue scenarios

Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms

This article covers radar signal processing for sensing in the context of assisted living (AL). This is presented through three example applications: human activity recognition (HAR) for activities of daily living (ADL), respiratory disorders, and sleep stages (SSs) classification. The common challenge of classification is discussed within a framework of measurements/preprocessing, feature extraction, and classification algorithms for supervised learning. Then, the specific challenges of the three applications from a signal processing standpoint are detailed in their specific data processing and ad hoc classification strategies. Here, the focus is on recent trends in the field of activity recognition (multidomain, multimodal, and fusion), health-care applications based on vital signs (superresolution techniques), and comments related to outstanding challenges. Finally, this article explores challenges associated with the real-time implementation of signal processing/classification algorithms

Posture-specific breathing detection

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Exploration of Micro-Doppler Signatures Associated with Humans and Dogs using UWB Radar

The work in this thesis has been a part of the task of using a radar to separate between humans and animals in a alarm and surveillance context. For the radar to be able to separate between humans and animals it would use a classifier that rely on features extracted from the radar data. The thesis considers two types of targets; either a human or a dog, and by using micro-Doppler signature, determines some fundamen- tal features which can be the used to classify them. The micro-Doppler signature is the superposition of frequency modulations represented in the joint time and Doppler frequency domain, where the modulations are caused by dierent moving components associated with the desired target. The micro-Doppler signature has been widely used for radar classification. The thesis has succeeded in developing algorithms and a system to extract micro- Doppler signatures from targets. Signatures from both humans and dogs has been produced and some simple features extracted from them. The major problem with the signatures created is that the radars pulse repetition frequency is a limiting factor and causes aliasing in the Doppler spectrum that corrupts the signatures. This has limited the study of targets to slow moving humans and dogs. Three important features for classification was extracted from the micro-Doppler signature by calculating the gait-Doppler map. They are, i) the average Doppler fre- quency fav(or average radial velocity vav), ii) fundamental gait frequency fg and iii) the stride length Ls which is derived from the two former features. The result points towards the possibility to separate humans and dogs using these parameters. The reason is that since the dogs limbs are shorter than a human it also has shorter stride length at a specific speed. However, this may not be sucient for decisions to be made in a robust alarm system, since it can be fooled by a smart intruder that could for example take unnatural short steps and simulate a dogs combination of the aforementioned features. In addition the determination of features are sensitive to large changes in radial speed. This can be mitigated by preprossing before the calculation of the features. The conclusion is, that based on substantial measurements of signatures ( approx. 50 series) and calculations of the three features one has arrived at a fairly robust method to distinguish between the two type of target in this thesis

UWB Pulse Radar for Human Imaging and Doppler Detection Applications

We were motivated to develop new technologies capable of identifying human life through walls. Our goal is to pinpoint multiple people at a time, which could pay dividends during military operations, disaster rescue efforts, or assisted-living. Such system requires the combination of two features in one platform: seeing-through wall localization and vital signs Doppler detection. Ultra-wideband (UWB) radar technology has been used due to its distinct advantages, such as ultra-low power, fine imaging resolution, good penetrating through wall characteristics, and high performance in noisy environment. Not only being widely used in imaging systems and ground penetrating detection, UWB radar also targets Doppler sensing, precise positioning and tracking, communications and measurement, and etc. A robust UWB pulse radar prototype has been developed and is presented here. The UWB pulse radar prototype integrates seeing-through imaging and Doppler detection features in one platform. Many challenges existing in implementing such a radar have been addressed extensively in this dissertation. Two Vivaldi antenna arrays have been designed and fabricated to cover 1.5-4.5 GHz and 1.5-10 GHz, respectively. A carrier-based pulse radar transceiver has been implemented to achieve a high dynamic range of 65dB. A 100 GSPS data acquisition module is prototyped using the off-the-shelf field-programmable gate array (FPGA) and analog-to-digital converter (ADC) based on a low cost solution: equivalent time sampling scheme. Ptolemy and transient simulation tools are used to accurately emulate the linear and nonlinear components in the comprehensive simulation platform, incorporated with electromagnetic theory to account for through wall effect and radar scattering. Imaging and Doppler detection examples have been given to demonstrate that such a “Biometrics-at-a-glance” would have a great impact on the security, rescuing, and biomedical applications in the future