将行为上下文与模型预测对齐，提升动作识别准确率
Process-aware Human Activity Recognition
- 用上下文过程模型对齐机器学习的事件概率
- 在实验中准确率和宏F1均优于基线模型
- 适合需要理解行为流程的场景如智能监控
人类在日常活动中自然遵循特定模式，这些模式由生产流程、社会规范和日常习惯等上下文驱动。传统动作识别（HAR）算法依赖神经网络或机器学习分析数据内在关系，但常忽略数据生成的上下文信息，影响性能。本文提出一种新方法，将机器学习生成的概率事件与从上下文推导出的过程模型对齐，通过自适应加权融合两种信息源，优化识别准确率。实验表明，该方法在多个基准上显著提升准确率与宏F1分数。
原文摘要 · Abstract (English)
Humans naturally follow distinct patterns when conducting their daily activities, which are driven by established practices and processes, such as production workflows, social norms and daily routines. Human activity recognition (HAR) algorithms usually use neural networks or machine learning techniques to analyse inherent relationships within the data. However, these approaches often overlook the contextual information in which the data are generated, potentially limiting their effectiveness. We propose a novel approach that incorporates process information from context to enhance the HAR performance. Specifically, we align probabilistic events generated by machine learning models with process models derived from contextual information. This alignment adaptively weighs these two sources of information to optimise HAR accuracy. Our experiments demonstrate that our approach achieves better accuracy and Macro F1-score compared to baseline models.
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