Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway.
Sensors (Basel). 2023 Jun 22;23(13):5810. doi: 10.3390/s23135810.
RF sensing offers an unobtrusive, user-friendly, and privacy-preserving method for detecting accidental falls and recognizing human activities. Contemporary RF-based HAR systems generally employ a single monostatic radar to recognize human activities. However, a single monostatic radar cannot detect the motion of a target, e.g., a moving person, orthogonal to the boresight axis of the radar. Owing to this inherent physical limitation, a single monostatic radar fails to efficiently recognize orientation-independent human activities. In this work, we present a complementary RF sensing approach that overcomes the limitation of existing single monostatic radar-based HAR systems to robustly recognize orientation-independent human activities and falls. Our approach used a distributed mmWave MIMO radar system that was set up as two separate monostatic radars placed orthogonal to each other in an indoor environment. These two radars illuminated the moving person from two different aspect angles and consequently produced two time-variant micro-Doppler signatures. We first computed the mean Doppler shifts (MDSs) from the micro-Doppler signatures and then extracted statistical and time- and frequency-domain features. We adopted feature-level fusion techniques to fuse the extracted features and a support vector machine to classify orientation-independent human activities. To evaluate our approach, we used an orientation-independent human activity dataset, which was collected from six volunteers. The dataset consisted of more than 1350 activity trials of five different activities that were performed in different orientations. The proposed complementary RF sensing approach achieved an overall classification accuracy ranging from 98.31 to 98.54%. It overcame the inherent limitations of a conventional single monostatic radar-based HAR and outperformed it by 6%.
射频感应提供了一种非侵入式、用户友好且保护隐私的方法，可用于检测意外跌倒和识别人体活动。当代基于射频的 HAR 系统通常使用单个单基地雷达来识别人体活动。然而，单个单基地雷达无法检测目标的运动，例如，与雷达视轴正交的移动人。由于这种固有的物理限制，单个单基地雷达无法有效地识别与方向无关的人体活动。在这项工作中，我们提出了一种互补的射频感应方法，克服了现有基于单基地雷达的 HAR 系统的局限性，能够稳健地识别与方向无关的人体活动和跌倒。我们的方法使用了分布式毫米波 MIMO 雷达系统，该系统由两个彼此正交放置的独立单基地雷达组成，设置在室内环境中。这两个雷达从两个不同的侧面角度照射移动的人，从而产生两个时变的微多普勒特征。我们首先从微多普勒特征中计算平均多普勒频移（MDS），然后提取统计和时频域特征。我们采用特征级融合技术融合提取的特征，并采用支持向量机对与方向无关的人体活动进行分类。为了评估我们的方法，我们使用了一个与方向无关的人体活动数据集，该数据集是从六个志愿者那里收集的。该数据集包含五个不同活动在不同方向上进行的超过 1350 次活动试验。所提出的互补射频感应方法的总体分类准确率在 98.31%到 98.54%之间。它克服了传统基于单基地雷达的 HAR 的固有局限性，性能优于后者 6%。
