通过对比对齐融合时序与图像特征，提升智能家居中日常活动识别的准确率与鲁棒性。
CARE: Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams
- 设计时序-图像对比对齐机制，联合优化双视角表示学习。
- 在米兰、开罗、京都7个数据集上分别达到89.8%、88.9%、73.3%准确率。
- 适合关注智能养老与传感器系统鲁棒性的研究者和开发者。
从事件触发的环境传感器流中识别日常生活活动（ADL）是辅助生活领域的重要任务，但现有方法受限于表征层面的不足。基于序列的方法保留了传感器激活的时间顺序，但对噪声敏感且缺乏空间感知；基于图像的方法捕捉全局模式与隐含空间关联，却压缩了细粒度时间动态并扭曲传感器布局。简单的融合方式（如特征拼接）无法强制两种表示视图之间的对齐，未能充分发挥其互补优势。我们提出面向事件触发传感器流的日常活动识别对比对齐框架（CARE），通过序列-图像对比对齐（SICA）与交叉熵分类联合优化，实现跨表示对齐与任务判别性。CARE整合了（i）时间感知、抗噪的序列编码，（ii）空间感知与频率敏感的图像表征，并采用（iii）联合对比-分类目标，实现端到端的对齐与判别嵌入学习。在三个CASAS数据集上评估，CARE达到领先性能（米兰89.8%、开罗88.9%、京都7 73.3%），并在传感器故障与布局变化下表现稳健，展现出在智能家居中可靠进行ADL识别的潜力。代码已开源：https://github.com/Jhziiiig/CARE。
原文摘要 · Abstract (English)
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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