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

Radar Sensing in Assisted Living: An Overview

This paper gives an overview of trends in radar sensing for assisted living. It focuses on signal processing and classification, looking at conventional approaches, deep learning and fusion techniques. The last section shows examples of classification in human activity recognition and medical applications, e.g. breathing disorder and sleep stages recognition

Signal Processing Contributions to Contactless Monitoring of Vital Signs Using Radars

Vital signs are a group of biological indicators that show the status of the body’s life-sustaining functions. They provide an objective measurement of the essential physiological functions of a living organism, and their assessment is the critical first step for any clinical evaluation. Monitoring vital sign information provides valuable insight into the patient's condition, including how they are responding to medical treatment and, more importantly, whether the patient is deteriorating. However, conventional contact-based devices are inappropriate for long-term continuous monitoring. Besides mobility restrictions and stress, they can cause discomfort, and epidermal damage, and even lead to pressure necrosis. On the other hand, the contactless monitoring of vital signs using radar devices has several advantages. Radar signals can penetrate through different materials and are not affected by skin pigmentation or external light conditions. Additionally, these devices preserve privacy, can be low-cost, and transmit no more power than a mobile phone. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios. The challenge stems from the fact that when we breathe, or when the heart beats, the tiny induced motion of the chest wall surface can be smaller than one millimeter. This means that the vital sign information can be easily lost in the background noise, or even masked by additional body movements from the monitored subject. This thesis aims to propose innovative signal processing solutions to enable the contactless monitoring of vital signs in practical scenarios. Its main contributions are threefold: a new algorithm for recovering the chest wall movements from radar signals; a novel random body movement and interference mitigation technique; and a simple, yet robust and accurate, adaptive estimation framework. These contributions were tested under different operational conditions and scenarios, spanning ideal simulation settings, real data collected while imitating common working conditions in an office environment, and a complete validation with premature babies in a critical care environment. The proposed algorithms were able to precisely recover the chest wall motion, effectively reducing the interfering effects of random body movements, and allowing clear identification of different breathing patterns. This capability is the first step toward frequency estimation and early non-invasive diagnosis of cardiorespiratory problems. In addition, most of the time, the adaptive estimation framework provided breathing and heart rate estimates within the predefined error intervals, being capable of tracking the reference values in different scenarios. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a complete contactless solution for vital signs monitoring

Aeronautical engineering: A continuing bibliography with indexes (supplement 260)

This bibliography lists 405 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Hybrid structures for molecular level sensing

With substantial molecular mobility and segment dynamics relative to metals and ceramics, all polymeric materials, to some extent, are stimuli-responsive by exhibiting pronounced chemical and physical changes in the backbone, side chains, segments, or end groups induced by changes in the local environment. Thus, the push to incorporate polymeric materials as sensing/responsive nanoscale layers into next-generation miniaturized sensor applications is a natural progression. The significance and impact of this research is wide-ranging because it offers design considerations and presents results in perhaps two of the most critical broad areas of nanotechnology: ultrathin multifunctional polymer coatings and miniaturized sensors. In this work, direct evidence is given showing that polymer coatings comprised of deliberately selected molecular segments with very different chemistry can have switchable properties, and that the surface composition can be precisely controlled, and thus properties can be tuned: all in films on the order of 20 nm and less. Furthermore, active sensing layers in the form of plasma-polymerized polymers are successfully incorporated into actual silicon based microsensors resulting in a novel hybrid organic/inorganic materials platform for microfabricated MEMS sensors with record performance far beyond contemporary sensors in terms of detection sensitivity to various environments. The results produced in this research show thermal sensors with more than two orders of magnitude better sensitivity than what is attainable currently. In addition, a humidity response on the order of parts per trillion, which is four orders of magnitude more sensitive than current designs is achieved. Molecular interactions and forces for organic molecules are characterized at the picoscale to optimize polymeric nanoscale layer design that in turn optimize and lead to microscale hybrid sensors with unprecedented sensitivities