面向真实世界情境感知人类活动识别的深度异构对比超图学习
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition
AI总结：
针对真实情境下HAR的多标签和信号变异问题，提出DHC-HGL框架，通过定制超图卷积层和对比损失捕获异构超图属性，在两项数据集上显著超越基线。
摘要：
人类活动识别（HAR）是一个具有挑战性的多标签分类问题，因为活动可能同时发生，且同一活动对应的传感器信号在不同情境下（如不同的设备放置位置）可能有所不同。本文提出了一种深度异构对比超图学习（DHC-HGL）框架，该框架以消息传递和邻域聚合的方式捕获异构的情境感知HAR（CA-HAR）超图属性。先前的工作仅探索了同构图或浅层节点异构图。DHC-HGL通过创新性地1）构建三种不同类型的子超图，每种子超图分别通过为处理边异构性而设计的不同定制HyperGraph Convolution（HGC）层，以及2）采用对比损失函数来确保节点异构性，从而处理异构CA-HAR数据。在两个CA-HAR数据集上的严格评估中，DHC-HGL在Matthews Correlation Coefficient（MCC）上比最先进的基线方法显著高出5.8%至16.7%，在Macro F1分数上高出3.0%至8.4%。此外，还展示了学习到的CA-HAR节点嵌入的UMAP可视化，以增强模型的可解释性。
英文摘要：
Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different device placements). This paper proposes a Deep Heterogeneous Contrastive Hyper-Graph Learning (DHC-HGL) framework that captures heterogenous Context-Aware HAR (CA-HAR) hypergraph properties in a message-passing and neighborhood-aggregation fashion. Prior work only explored homogeneous or shallow-node-heterogeneous graphs. DHC-HGL handles heterogeneous CA-HAR data by innovatively 1) Constructing three different types of sub-hypergraphs that are each passed through different custom HyperGraph Convolution (HGC) layers designed to handle edge-heterogeneity and 2) Adopting a contrastive loss function to ensure node-heterogeneity. In rigorous evaluation on two CA-HAR datasets, DHC-HGL significantly outperformed state-of-the-art baselines by 5.8% to 16.7% on Matthews Correlation Coefficient (MCC) and 3.0% to 8.4% on Macro F1 scores. UMAP visualizations of learned CA-HAR node embeddings are also presented to enhance model explainability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
