门控适应用于人体活动识别中的持续学习
Gated Adaptation for Continual Learning in Human Activity Recognition
AI总结：
本文提出一种基于通道级门控调制的持续学习框架，通过特征选择而非生成来减少HAR中的遗忘，提升准确率并降低参数训练量。
摘要：
在物联网生态系统中，可穿戴传感器越来越多地支持远程健康监测、老年人护理和智能家庭自动化等应用，这些应用都依赖于稳健的人体活动识别（HAR）。持续学习系统必须在可塑性（学习新任务）与稳定性（保留先前知识）之间取得平衡，但AI模型往往表现出灾难性遗忘，即学习新任务会损害早期任务的性能。这一挑战在领域增量HAR中尤为突出，其中设备上的模型必须适应具有不同运动模式的新主体，同时在不将敏感数据传输到云中时保持对先前主体的准确性。我们提出了一种基于通道级门控调制冻结预训练表示的参数高效持续学习框架。我们的关键见解是适应应通过特征选择而不是特征生成进行：通过限制学习转换为现有特征的对角线缩放，我们保留了预训练表示的几何结构，同时启用主体特定的调制。我们提供了一个理论分析，表明门控实现了有限的对角线算子，将其表示性漂移限制在不受约束的线性变换之下。经验上，冻结骨干网络显著减少了遗忘，轻量级门控恢复了丢失的适应能力，实现了稳定性与可塑性的同时。在PAMAP2上，我们的方法将遗忘率从39.7%降低到16.2%，最终准确率从56.7%提高到77.7%，同时训练的参数不到2%。我们的方法在没有回放缓冲区或任务特定正则化的情况下匹配或超过了标准持续学习基线，证实了结构化对角线算子在分布偏移下的有效性和效率。
英文摘要：
Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR). Continual learning systems must balance plasticity (learning new tasks) with stability (retaining prior knowledge), yet AI models often exhibit catastrophic forgetting, where learning new tasks degrades performance on earlier ones. This challenge is especially acute in domain-incremental HAR, where on-device models must adapt to new subjects with distinct movement patterns while maintaining accuracy on prior subjects without transmitting sensitive data to the cloud. We propose a parameter-efficient continual learning framework based on channel-wise gated modulation of frozen pretrained representations. Our key insight is that adaptation should operate through feature selection rather than feature generation: by restricting learned transformations to diagonal scaling of existing features, we preserve the geometry of pretrained representations while enabling subject-specific modulation. We provide a theoretical analysis showing that gating implements a bounded diagonal operator that limits representational drift compared to unconstrained linear transformations. Empirically, freezing the backbone substantially reduces forgetting, and lightweight gates restore lost adaptation capacity, achieving stability and plasticity simultaneously. On PAMAP2 with 8 sequential subjects, our approach reduces forgetting from 39.7% to 16.2% and improves final accuracy from 56.7% to 77.7%, while training less than 2% of parameters. Our method matches or exceeds standard continual learning baselines without replay buffers or task-specific regularization, confirming that structured diagonal operators are effective and efficient under distribution shift.
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。
