AI Characterisation of millimetre-Wave Signals from Human-centric Mechanical Actions applicable to Paediatric Neurological Diagnosis

近年来，研究人员对使用雷达的几个高频波段提取某些医学信息（如心肺功能）产生了极大的兴趣。毫米波雷达具有非侵入性、非接触性和非电离性，因此非常适合在儿科医学中应用。使用毫米波段雷达信号，可以更可靠地检测、监测和分类心脏和肺的非常小的振动，包括其他身体部位的运动。这项研究的主要重点是部署深度学习算法，以表征这两个重要人体器官的雷达信号输出以及某些身体姿势，这对研究和识别婴儿神经发育至关重要。通过医疗人工智能和雷达技术的结合，该项目还将研究识别人类行为所揭示的一种或两种神经系统疾病的前景：在成功表征伴随的雷达信号之后。

The use of several high frequency bands of radar for the extraction of some medical information (such as cardiorespiratory features) has in recent years been of huge interest to researchers. Millimetre-wave radar is non-invasive, non-contact and non-ionising, therefore very suitable for applications in paediatric medicine. Very small vibrations of the heart and lung including movements of other body parts can be detected, monitored and classified with greater reliability using the millimetre-band radar signals. The primary focus of this research is the deployment of deep learning algorithms to characterising radar signal outputs of these two vital human organs together with certain body gestures which are critical to the study and recognition of infantile neurological development. Through a combination of medical AI and radar technology, the project will also examine the prospect of identifying one or two neurological conditions as revealed by human-induced actions: following successful characterisation of attendant radar signals.