Artificial Intelligence enabled investigation of biometric data using radar systems for advancements in assistive technologies

在这个项目中，我们的目标是开发一个人工智能支持的超宽带（UWB）雷达系统，其目的是对人类生物特征数据进行非侵入性测量和预测。这类项目的主要应用是在老年人家中用于检测严重和突发的健康问题。本研究还探讨了在封闭环境中检测多人的新方法。这种系统所测量和预测的数据无疑可以用于多种应用；例如，在工业环境中，对特定区域内人员存在的自动感知是一个非常安全的问题。在商业环境中，视觉摄像头数据是一个重要的数据安全问题来源，例如，了解多人的存在和行为可以用来使周围环境更加节能。该系统的主要和主要用途是使用雷达技术测量和识别房间内个人或个人的人类心率。利用这些信息和机器学习算法，该系统应该能够识别异常或任何其他重大问题，例如心脏病发作或心脏突然停止跳动。这是一种预防性报警系统，用于特别容易出现突发疾病的个人，主要是老年人或婴儿。这个博士项目专门研究嵌入式系统，利用人工智能能力来分析固有的微弱和嘈杂的雷达信号。该项目希望回答与使用机器学习进行信号处理有关的问题；主要是，有没有有效的方法来分析微弱的雷达信号，以进行生物特征数据测量？雷达系统能否被改进到可能探测脑电波的程度？为了尝试解决这个问题，我们必须对手头的问题有一个整体的看法，并理解这个项目结合了计算机科学、电气工程和医学科学领域。我们将首先开发雷达系统，它是获取指定数据的组成部分，然后将硬件改进到可以进行测量的程度。利用这些获得的信息，从医学角度理解数据并将这种理解构建到可能开发用于分析的任何计算算法中是很重要的。最后，必须对系统进行彻底的测试，以最大限度地提高其识别生物特征数据模式的效率。为了成功实施这个项目，我们必须开发新的信号处理算法，并将机器学习应用于它们。我们还必须确定从微弱和嘈杂的雷达信号中识别和处理生物特征数据的优化技术。最后，我们应该研究这种固定雷达系统最终能够探测脑电波的可能性。这些是本博士项目要解决的一些主要技术挑战。

In this project, we aim to develop an artificial intelligence enabled ultra-wideband (UWB) radar system, the purpose of which would be for the non-invasive measurement and prediction of human biometric data. The primary application for this type of project is for uses in elderly people's homes for the detection of severe and sudden health issues. This research also investigates into novel methods of detecting multiple people within closed environments. The data measured and predicted by such a system can undoubtedly be used for a multitude of applications; in industrial settings for example, automated awareness on the presence of people within specific areas is a matter of great security and safety. Within commercial environments, where visual camera data is a source of great data security concern, understanding the presence and behaviour of multiple people can, for example, be used to make the surroundings more energy efficient. The main and primary use of the system is to measure and recognise the human heart rate of an individual or individuals within a room using radar technology. Using this information, and machine learning algorithms, the system should then be able to recognise abnormalities or any other major issues, such as a heart attacks or the sudden stopping of the heart completely. This is to be used as a preventative alarm system for individuals particularly prone to sudden medical conditions, mainly speaking elderly people or infants.This PhD project is specifically looking into embedded systems utilising artificial intelligence capabilities to analyse inherently weak and noisy radar signals. This project hopes to answer questions related to using machine learning for the purpose of signal processing; mainly being, are there any effective methods of analysing weak radar signals for the biometric data measurements? And can a radar system be refined to possibly detect brain waves? To attempt to solve this problem, we must take a holistic view of the questions at hand and understand that this project combines areas of computer science, electrical engineering and the medical sciences. We will begin by developing the radar system, which is integral to the acquisition of the specified data and then refining the hardware to the point where measurements can be made. Using this acquired information, it is important to understand the data from a medical perspective and build this understanding into any computational algorithms that may be developed for analysis. Finally, thorough testing must be done on the system in order to maximise its efficiency in recognising patterns within the biometric data.To successfully carry out this project, we must develop novel signal processing algorithms and apply machine learning to them. We must also identify optimized techniques for identifying and processing biometric data from weak and noisy radar signals. Finally, we should investigate into the possibility of this stationary radar system being capable of ultimately detecting brain waves. These are some of the major technical challenges to be addressed by this PhD project.