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.

翻译：人类活动识别（HAR）是众多以人为中心的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，但多模态数据混合、活动异质性以及复杂模型部署等问题在很大程度上仍未得到解决。本文旨在解决基于传感器的人类活动识别中的多模态数据混合、活动异质性及复杂模型部署等问题。我们提出了一种时空注意力模态分解对齐融合策略，以应对传感器数据混合分布的问题。通过跨模态时空解耦表示捕获活动的关键判别特征，并结合梯度调制以缓解数据异质性。此外，构建了一个可穿戴部署仿真系统。我们在大量公共数据集上进行了实验，验证了模型的有效性。