标题： USAD：一种无监督数据增强时空注意力扩散网络 显示英文标题

标题： USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network

摘要： 人类活动识别（HAR）的主要目标是从传感器数据中推断正在进行的人类动作，这项任务在健康监测、安全保护和运动分析中有着广泛的应用。 尽管研究层出不穷，HAR仍然面临一些关键挑战，包括稀有活动的标记样本不足、高层次特征提取不足以及在轻量级设备上的模型性能不佳。 为了解决这些问题，本文提出了一种以多注意力交互机制为核心的综合优化方法。 首先，采用一种无监督的、统计引导的扩散模型进行数据增强，从而缓解标记数据稀缺和严重类别不平衡的问题。 其次，设计了一个多分支时空交互网络，通过并行的带有3*3、5*5和7*7卷积核的残差分支捕捉序列数据的多尺度特征。 同时，引入时间注意力机制以识别关键时间点，而空间注意力则增强了传感器间的交互。 进一步引入了跨分支特征融合单元，以提高整体特征表示能力。 最后，集成了自适应多损失函数融合策略，允许动态调整损失权重和整体模型优化。 在三个公开数据集WISDM、PAMAP2和OPPORTUNITY上的实验结果表明，所提出的无监督数据增强时空注意力扩散网络（USAD）分别达到了98.84%、93.81%和80.92%的准确率，显著优于现有方法。 此外，在嵌入式设备上的实际部署验证了所提方法的效率和可行性。 显示英文摘要

摘要： The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensive optimization approach centered on multi-attention interaction mechanisms. First, an unsupervised, statistics-guided diffusion model is employed to perform data augmentation, thereby alleviating the problems of labeled data scarcity and severe class imbalance. Second, a multi-branch spatio-temporal interaction network is designed, which captures multi-scale features of sequential data through parallel residual branches with 3*3, 5*5, and 7*7 convolutional kernels. Simultaneously, temporal attention mechanisms are incorporated to identify critical time points, while spatial attention enhances inter-sensor interactions. A cross-branch feature fusion unit is further introduced to improve the overall feature representation capability. Finally, an adaptive multi-loss function fusion strategy is integrated, allowing for dynamic adjustment of loss weights and overall model optimization. Experimental results on three public datasets, WISDM, PAMAP2, and OPPORTUNITY, demonstrate that the proposed unsupervised data augmentation spatio-temporal attention diffusion network (USAD) achieves accuracies of 98.84%, 93.81%, and 80.92% respectively, significantly outperforming existing approaches. Furthermore, practical deployment on embedded devices verifies the efficiency and feasibility of the proposed method.