Fall behavior is closely related to the high mortality rate of the elderly, so fall detection has become an important and urgent research area in human behavior recognition. However, the existing fall detection methods, suffer from the loss of detailed action information during feature extraction due to the downsampling operation, resulting in subpar performance when detecting falls with similar behaviors such as lying and sitting. To solve the challenges, this study proposes a high-resolution spatio-temporal feature extraction method based on a spatio-temporal coordinate attention mechanism. The method employs 3D convolutions to extract spatio-temporal features and utilizes gradual down-sampling to generate a multi-resolution sub-network, thus realizing multi-scale fusion and perception enhancement of details. In particular, this study designs a pseudo-3D basic block, which simulates the ability of 3D convolution, to ensure the running speed of the network while controlling the number of parameters. Further, a spatio-temporal coordinate attention mechanism is designed to accurately extract the spatio-temporal positional changes of key skeletal points and the interrelationships among them. Long-term dependencies in horizontal, vertical, temporal directions are captured through three one-dimensional global pooling operations. Then the long-range relationships and channel correlations among features are captured by cascading and slicing operations. Finally, the key information is effectively highlighted by performing dot-multiplication operations between the feature maps from the horizontal, vertical and temporal directions and the input feature maps. Experimental results on three typical public datasets show that the proposed method can better extract motion features and improve the accuracy of fall detection.

跌倒行为与老年人的高死亡率密切相关，因此跌倒检测已成为人类行为识别中一个重要而紧迫的研究领域。然而，现有的跌倒检测方法由于下采样操作，在特征提取过程中会丢失详细的动作信息，导致在检测具有类似行为（如躺着和坐着）的跌倒时性能不佳。为了解决这些挑战，该文提出了一种基于时空坐标注意力机制的高分辨率时空特征提取方法。该方法采用三维卷积提取时空特征，并利用渐进降采样生成多分辨率子网络，从而实现细节的多尺度融合和感知增强。特别是，本研究设计了一个伪 3D 基本块，它模拟了 3D 卷积的能力，在控制参数数量的同时保证了网络的运行速度。进一步，设计了一种时空坐标注意力机制，以准确提取关键骨骼点的时空位置变化及其之间的相互关系。水平、垂直、时间方向的长期依赖关系通过三个一维全局池化操作来捕获。然后，通过级联和切片操作捕获特征之间的长期关系和通道相关性。最后，通过在水平、垂直和时间方向的特征图与输入特征图之间执行点乘运算，有效地突出了关键信息。 在3个典型公共数据集上的实验结果表明，所提方法能够更好地提取运动特征，提高跌倒检测的准确性。