面向活动识别的带辅助用户检测的对比学习
Contrastive Learning with Auxiliary User Detection for Identifying Activities
AI总结：
针对现有HAR研究忽略用户感知、无法缓解用户间动作差异的问题，提出CLAUDIA框架，通过联合学习UI与CA-HAR任务、引入监督对比损失提升表征质量，在三个数据集上实现显著性能提升。
摘要：
人类活动识别（HAR）是普适计算中的核心任务，具备广泛的实际应用价值。尽管当前最先进（SOTA）的HAR研究已取得亮眼性能，但若干关键方向仍未得到充分探索。首先，HAR既具备强上下文相关性，也具备高度个性化特征，但现有研究大多聚焦于上下文感知（CA）能力，却普遍忽略了用户感知（UA）的必要性。我们认为，在HAR任务中，处理用户先天动作执行差异的影响，与考虑外部上下文环境设置同等重要。其次，用户感知虽能让模型识别用户间的差异，但并不必然保证能缓解这些差异，即无法确保同一活动下的预测结果统一，因此需要一种能明确拉近「不同用户、相同活动」表征的方法。为填补这一空白，我们提出了CLAUDIA——一个旨在解决上述问题的新型框架。具体而言，我们通过将用户识别（UI）整合到上下文感知HAR（CA-HAR）框架中，拓展了CA-HAR任务的上下文范围，在名为用户与上下文感知HAR（UCA-HAR）的新任务中联合预测CA-HAR和UI结果。该方法通过联合学习用户不变模式和用户特定模式，丰富了个性化与上下文理解能力。受视觉领域SOTA设计启发，我们在实例-实例对上引入监督对比损失目标，以提升模型效能与所学特征的质量。在三个真实世界CA-HAR数据集上的评估结果显示，模型性能得到大幅提升：马修斯相关系数平均提升5.8%至14.1%，宏F1分数平均提升3.0%至7.2%。
英文摘要：
Human Activity Recognition (HAR) is essential in ubiquitous computing, with far-reaching real-world applications. While recent SOTA HAR research has demonstrated impressive performance, some key aspects remain under-explored. Firstly, HAR can be both highly contextualized and personalized. However, prior work has predominantly focused on being Context-Aware (CA) while largely ignoring the necessity of being User-Aware (UA). We argue that addressing the impact of innate user action-performing differences is equally crucial as considering external contextual environment settings in HAR tasks. Secondly, being user-aware makes the model acknowledge user discrepancies but does not necessarily guarantee mitigation of these discrepancies, i.e., unified predictions under the same activities. There is a need for a methodology that explicitly enforces closer (different user, same activity) representations. To bridge this gap, we introduce CLAUDIA, a novel framework designed to address these issues. Specifically, we expand the contextual scope of the CA-HAR task by integrating User Identification (UI) within the CA-HAR framework, jointly predicting both CA-HAR and UI in a new task called User and Context-Aware HAR (UCA-HAR). This approach enriches personalized and contextual understanding by jointly learning user-invariant and user-specific patterns. Inspired by SOTA designs in the visual domain, we introduce a supervised contrastive loss objective on instance-instance pairs to enhance model efficacy and improve learned feature quality. Evaluation across three real-world CA-HAR datasets reveals substantial performance enhancements, with average improvements ranging from 5.8% to 14.1% in Matthew's Correlation Coefficient and 3.0% to 7.2% in Macro F1 score.
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