提出跨模态解耦方法，提升可穿戴设备上人体动作识别准确率。
CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition
- 通过时空注意力分解融合，分离多传感器数据混合特征。
- 在多个公开数据集上实现比现有方法更高的识别精度。
- 专为可穿戴设备部署设计，适合边缘计算场景应用。
人体动作识别（HAR）是众多以人为中心智能应用的基础技术。尽管深度学习方法已被用于加速特征提取，但多模态数据混杂、动作异质性以及复杂模型部署等问题仍未有效解决。本文旨在解决基于传感器的HAR中多模态数据混杂、动作异质性及模型部署复杂等挑战。提出一种时空注意力模态分解对齐融合策略，以应对传感器数据分布混杂问题。通过跨模态时空解耦表示，捕获动作的关键判别特征，并结合梯度调制缓解数据异质性。此外，构建了可穿戴部署仿真系统。在大量公开数据集上进行实验，验证了模型的有效性。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) is a fundamental technology for numerous human - centered intelligent applications. Although deep learning methods have been utilized to accelerate feature extraction, issues such as multimodal data mixing, activity heterogeneity, and complex model deployment remain largely unresolved. The aim of this paper is to address issues such as multimodal data mixing, activity heterogeneity, and complex model deployment in sensor-based human activity recognition. We propose a spatiotemporal attention modal decomposition alignment fusion strategy to tackle the problem of the mixed distribution of sensor data. Key discriminative features of activities are captured through cross-modal spatio-temporal disentangled representation, and gradient modulation is combined to alleviate data heterogeneity. In addition, a wearable deployment simulation system is constructed. We conducted experiments on a large number of public datasets, demonstrating the effectiveness of the model.
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