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

近年来，利用雷达的几个高频频段来提取一些医学信息(如心肺特征)引起了研究人员的极大兴趣。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极微小振动，包括身体其他部分的运动。这项研究的主要焦点是利用深度学习算法来表征这两个重要人体器官的雷达信号输出以及对研究和识别婴儿神经发育至关重要的某些身体姿势。通过医学人工智能和雷达技术的结合，该项目还将研究识别一到两种神经疾病的前景，这些疾病是由人类诱导的行为揭示的：在成功地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.