标题： 基于置信度的梯度调制用于多模态人类活动识别：一种动态对比双路径学习方法 显示英文标题

标题： Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach

摘要： 基于传感器的人类活动识别（HAR）是一项核心技术，使智能系统能够感知并与其环境互动。 然而，多模态HAR系统仍然面临关键挑战，例如跨模态特征对齐的困难和模态贡献的不平衡。 为了解决这些问题，我们提出了一种名为动态对比双路径网络（DCDP-HAR）的新框架。 该框架包含三个关键组件。 首先，采用双路径特征提取架构，其中ResNet和DenseNet分支协作处理多模态传感器数据。 其次，引入多阶段对比学习机制，以实现从局部感知到语义抽象的渐进对齐。 第三，我们提出了一种基于置信度的梯度调制策略，在反向传播过程中动态监控并调整每个模态分支的学习强度，有效缓解模态竞争。 此外，采用基于动量的梯度累积策略以增强训练稳定性。 我们进行了消融研究以验证每个组件的有效性，并在四个公开基准数据集上进行了广泛的比较实验。 显示英文摘要

摘要： Sensor-based Human Activity Recognition (HAR) is a core technology that enables intelligent systems to perceive and interact with their environment. However, multimodal HAR systems still encounter key challenges, such as difficulties in cross-modal feature alignment and imbalanced modality contributions. To address these issues, we propose a novel framework called the Dynamic Contrastive Dual-Path Network (DCDP-HAR). The framework comprises three key components. First, a dual-path feature extraction architecture is employed, where ResNet and DenseNet branches collaboratively process multimodal sensor data. Second, a multi-stage contrastive learning mechanism is introduced to achieve progressive alignment from local perception to semantic abstraction. Third, we present a confidence-driven gradient modulation strategy that dynamically monitors and adjusts the learning intensity of each modality branch during backpropagation, effectively alleviating modality competition. In addition, a momentum-based gradient accumulation strategy is adopted to enhance training stability. We conduct ablation studies to validate the effectiveness of each component and perform extensive comparative experiments on four public benchmark datasets.