Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-01-12 DOI:10.1145/3631444
Wen Ge, Guanyi Mou, Emmanuel O. Agu, Kyumin Lee
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Abstract

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. Our code is publicly available1 to encourage further research.
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用于野外上下文感知人类活动识别的深度异构对比超图学习
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,而且在不同的情境下(如不同的设备位置),对应于同一活动的传感器信号也可能不同。本文提出了一种深度异构对比超图学习(DHC-HGL)框架,以消息传递和邻域聚合的方式捕捉异构情境感知 HAR(CA-HAR)超图属性。之前的工作只探索了同构或浅节点异构图。DHC-HGL 处理异构 CA-HAR 数据的创新方法是:1)构建三种不同类型的子超图,分别通过不同的自定义超图卷积(HGC)层来处理边缘异构性;2)采用对比损失函数来确保节点异构性。在两个 CA-HAR 数据集上进行的严格评估中,DHC-HGL 在马修斯相关系数 (Matthews Correlation Coefficient, MCC) 和 Macro F1 分数上分别以 5.8% 至 16.7% 和 3.0% 至 8.4% 的优势明显优于最先进的基线。为了提高模型的可解释性,我们还展示了所学 CA-HAR 节点嵌入的 UMAP 可视化效果。我们的代码是公开的1,以鼓励进一步的研究。
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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