AI 中文总结
本研究利用扩散模型生成合成传感器数据，通过预训练加微调策略提升CABiGRU在饮食活动识别中的性能，在DEO数据集上达到90.6%的平衡准确率，有效缓解类别不平衡问题。
AI 中文摘要
人类活动识别（HAR）在医疗保健、健康监测和日常监控应用中日益重要，其中检测进食和饮水等饮食行为可为饮食习惯和慢性病管理提供可操作的见解。然而，HAR系统在细微且代表性不足的类别上往往表现不佳，限制了其在现实饮食监测中的实用性。本工作基于CABiGRU——一种具有双向GRU层、多头注意力和残差连接的卷积架构，旨在从智能手表加速度计、陀螺仪和磁力计数据中捕捉判别性时间模式。为提高CaBiGRU的泛化能力并减少少数类别的欠拟合，我们利用扩散模型生成合成传感器数据窗口，并采用两阶段训练策略：先在合成数据上预训练CABiGRU，再在真实数据上进行微调。在DEO（饮水/进食/其他）数据集上，所提出的流程实现了90.6%的平衡准确率，优于强监督基线，展示了基于扩散模型的合成预训练在识别饮食行为方面的优势，并代表了处理不平衡类别的进步。这些结果表明，将扩散生成的数据与有针对性的微调相结合，可增强对饮食行为的稳健识别，支持在医疗保健和营养监测场景中更可靠的部署。
英文摘要
Human activity recognition (HAR) is increasingly important for healthcare, well-being, and daily monitoring ap- plications, for which detecting alimentary activities such as eating and drinking can provide actionable insight into dietary habits and chronic disease management. HAR systems, however, often underperform on subtle and underrepresented classes, limiting their utility in real-world dietary monitoring. This work builds upon CABiGRU, a convolutional architecture with Bidirectional GRU layers, multi-head attention, and residual connections, designed to capture discriminative temporal patterns from smart- watch accelerometer, gyroscope, and magnetometer data. To improve CaBiGRU's generalization and reduce underfitting in the minority class, we leverage synthetic sensor data windows using a diffusion model and adopt a two-stage training strategy: pre-training CABiGRU on synthetic data, followed by fine-tuning on the real-world data. On the DEO (drinking/eating/other) dataset, the proposed pipeline achieves a balanced accuracy of 90.6%, improving over a strong supervised baseline and showing the benefits of diffusion-based synthetic pre-training for recognizing alimentary activities and representing a step forward dealing with unbalanced classes. These results suggest that combining diffusion-generated data with targeted fine-tuning enhances robust recognition of dietary behaviors, supporting more reliable deployment in healthcare and nutrition-monitoring settings.
Comments6 pages, 3 figures, 1 table
