arXiv 2510.169882026-03-14cs.CVcs.AI

CARE：基于事件触发传感器流的ADL识别对比对齐

CARE: Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams

Electrical and Computer Engineering University of Maryland, College Park（马里兰大学电气与计算机工程学院）；School of Computing University of Georgia（佐治亚大学计算机科学学院）；Eindhoven University of Technology（埃因霍温理工大学）

Junhao Zhao, Zishuai Liu, Ruili Fang, Jin Lu, Linghan Zhang, Fei Dou

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AI总结：

本文提出CARE框架，通过序列-图像对比对齐和交叉熵分类联合优化，解决事件触发传感器流中ADL识别的表示对齐与任务判别性问题，实验表明其在三个CASAS数据集上取得最佳性能。

摘要：

从事件触发的环境传感器中识别日常活动（ADLs）是智能养老中的关键任务，但现有方法受限于表示层面的限制。基于序列的方法保留传感器激活的时序顺序，但对噪声敏感且缺乏空间意识；基于图像的方法捕捉全局模式和隐含的空间相关性，但压缩了细粒度的时间动态并扭曲传感器布局。简单的融合（如特征拼接）未能强制序列和图像表示视图之间的对齐，未能充分利用其互补优势。我们提出CARE，一个端到端框架，通过序列-图像对比对齐（SICA）和交叉熵分类联合优化，确保跨表示对齐和任务特定的判别性。CARE整合（i）时间感知、噪声鲁棒的序列编码，（ii）空间感知和频率敏感的图像表示，并采用（iii）联合对比-分类目标进行端到端学习，以获得对齐和判别的嵌入。在三个CASAS数据集上评估，CARE在米兰（89.8%）、开罗（88.9%）和京都7（73.3%）上取得最佳性能，并展示出对传感器故障和布局变化的鲁棒性，突显其在智能家庭中可靠ADL识别的潜力。我们开源代码在https://github.com/Jhziiiig/CARE。

英文摘要：

The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sensitive to noise and lack spatial awareness, while image-based approaches capture global patterns and implicit spatial correlations but compress fine-grained temporal dynamics and distort sensor layouts. Naive fusion (e.g., feature concatenation) fails to enforce alignment between sequence- and image-based representation views, underutilizing their complementary strengths. We propose Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams (CARE), an end-to-end framework that jointly optimizes representation learning via Sequence-Image Contrastive Alignment (SICA) and classification via cross-entropy, ensuring both cross-representation alignment and task-specific discriminability. CARE integrates (i) time-aware, noise-resilient sequence encoding with (ii) spatially-informed and frequency-sensitive image representations, and employs (iii) a joint contrastive-classification objective for end-to-end learning of aligned and discriminative embeddings. Evaluated on three CASAS datasets, CARE achieves state-of-the-art performance (89.8% on Milan, 88.9% on Cairo, and 73.3% on Kyoto7) and demonstrates robustness to sensor malfunctions and layout variability, highlighting its potential for reliable ADL recognition in smart homes. We release our code at https://github.com/Jhziiiig/CARE.

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