## 3 Model Introduction  
The YOLOv11 [20] model employed in this study is built upon the official open-source implementation provided by Ultralytics, utilizing its Python3.9.0 interface (ultralytics==11.0.0) for model training and inference. The core codebase is derived from its GitHub repository. The model follows a three-stage architecture consisting of a backbone, a Feature Pyramid Network (neck), and a Detection Head. The conventional C2f module is replaced with the C3k2 module, which incorporates small convolution kernels and cross-stage connections to enhance feature extraction accuracy while maintaining lightweight performance. Innovatively, the model integrates a Parallel Spatial Attention mechanism (C2 PSA) to improve adaptability in multi-object scenarios. Additionally, depthwise separable convolutions (DWConv) are deployed in the detection head, significantly reducing the number of parameters while preserving the advantages of multi-scale detection. This results in a synergistic improvement in both detection accuracy and inference speed. The model configuration in this study is based on the original YOLOv11n architecture, with the integration of the Efficient Channel Attention (ECA) mechanism ([PERSON] et al., 2020 [40]). ECA computes channel interaction weights using an adaptive 1D convolution kernel, effectively avoiding the computational redundancy associated with fully connected layers. This reduces the complexity of channel feature recalibration from O(C\({}^{2}\)) to O(C), thereby enhancing the representation of critical information. Figure 1 illustrates the architecture of the improved model. The black box in the C2 PSA module highlights the newly added ECA component, which is embedded at the end of the backbone in a plug-and-play manner. This integration enhances the model's small object detection performance without significantly increasing computational overhead. As shown in Figure 1, the detection and classification process of the YOLOv11-ECA model begins by feeding input image frames into the backbone network enhanced with the Efficient Channel Attention (ECA) module. This module improves the model's ability to represent both channel and spatial features, thereby enhancing the perception of key motion regions. The extracted multi-scale features are then fused and passed to the detection head, which outputs detection results, including bounding boxes, class probability distributions, and confidence scores. During classification, the model may predict multiple candidate bounding boxes for each frame, with each box associated with a probability distribution over eight yoga pose categories. The final classification result is determined by filtering out all boxes below a predefined confidence threshold and selecting the box  
Figure 1: YOLOv11+ECA model structure. with the highest confidence score as the representative pose for the current frame. The category corresponding to the highest class probability within this box is then assigned as the predicted pose. If no detection box meets the confidence threshold, the system concludes that no pose has been detected. This process ensures that the model accurately determines whether the current image contains a correct yoga pose based on the most reliable prediction. The C2 PSA module of the YOLOv11 model employs the SE (Squeeze-and-Excitation) channel attention mechanism, a sophisticated technique that enhances the model's capacity to discern significant features through the adaptable adjustment of channel weights. The specific steps involved are as follows: firstly, the spatial information of each channel is compressed into a scalar using global average pooling (Squeeze) to obtain the channel description vector \(z_{c}\). The channel attention coefficients \(s_{c}\) are then generated using a two-layer fully connected network (Excitation) modeling. Finally, the channel-by-channel multiplication of \(s_{c}\) with the original feature map is performed to achieve feature recalibration.