Behaviour dataset using a K-band UWB linear FMCW radar

Current radar fall detection techniques based on deep learning (DL) networks are often too complex for real-time detection. This paper proposes a real-time fall detection approach by reducing the complexity of the DL networks and the UWB radar hardware requirements. A multi-indoor scene behaviour dataset of 40 subjects is established using K-band UWB radar. A sliding window-based dataflow augmentation method is proposed to augment and balance the given datasets. A simple radar signal preprocessing model that is suitable for hardware with low sampling rates provides appropriately sized images of range-time spectrograms for DL. A lightweight DL network is designed to realise real-time fall detection for radar-embedded devices. The dataset division process follows the principle of mutual exclusion. The five-fold crossvalidation results (F1-score = 0.9896 ± 0.0047) and testing results obtained in a new scene (F1-score = 0.9872) confirm that only using 2-second range-time spectrogram images as inputs of the proposed network is sufficient for achieving good classification performance and strong generalisation ability. The proposed network size is 1.9178 Mb, with of 37.9978 M floating point operations (FLOPs) when the input image size of the radar range-time spectrograms is set to 112×112. Compared with currently popular DL networks, the proposed network achieves a good compromise between classification performance and computational complexity.

当前基于深度学习（Deep Learning，DL）网络的雷达跌倒检测技术，往往因结构过于复杂而难以适配实时检测场景。本文通过降低深度学习网络的复杂度与超宽带（Ultra Wideband，UWB）雷达的硬件需求，提出了一种实时跌倒检测方案。本研究借助K波段超宽带雷达，构建了涵盖40名受试者的多室内场景行为数据集。本文提出一种基于滑动窗口的数据流增强方案，用于扩充并平衡现有数据集。针对低采样率硬件设计的简易雷达信号预处理模型，可生成尺寸适配深度学习模型的距离-时间频谱图。本文设计了一款轻量级深度学习网络，可实现雷达嵌入式设备的实时跌倒检测。数据集划分严格遵循互斥原则。五折交叉验证结果（F1分数=0.9896±0.0047）与全新场景下的测试结果（F1分数=0.9872）证实，仅需以2秒时长的距离-时间频谱图作为所提网络的输入，即可实现优异的分类性能与极强的泛化能力。当雷达距离-时间频谱图的输入图像尺寸设为112×112时，所提网络的参数规模为1.9178 Mb，浮点运算量（Floating Point Operations，FLOPs）达37.9978 M。与当前主流的深度学习网络相比，所提网络在分类性能与计算复杂度之间实现了良好的权衡与平衡。