School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Sensors (Basel). 2024 Aug 15;24(16):5294. doi: 10.3390/s24165294.
To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time-distance images, time-distance images, and distance-distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%.
为了解决识别各种类型跌倒的挑战，这些跌倒通常具有很高的相似度，难以区分，本文提出了一种基于 SE-Residual Concatenate Network（SE-RCNet）的自适应加权融合的人体跌倒分类系统。首先，我们设计了创新的 SE-RCNet 网络，在密集和残差连接后引入 SE 模块，自动重新校准特征通道权重并抑制无关特征。随后，该网络用于训练和分类三种类型的雷达图像：时距图像、时距图像和距离距离图像。通过自适应融合这三种类型的雷达图像的分类结果，我们实现了更高的动作识别精度。实验结果表明，SE-RCNet 在我们自建的数据集上对三种雷达图像类型的 F1 得分分别达到了 94.0%、94.3%和 95.4%。应用自适应加权融合方法后，F1 得分进一步提高到 98.1%。
