Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR models have achieved impressive recognition performance. However, most HAR algorithms are susceptible to the multi-class windows problem that is essential yet rarely exploited. In this paper, we propose to relieve this challenging problem by introducing the segmentation technology into HAR, yielding joint activity segmentation and recognition. Especially, we introduce the Multi-Stage Temporal Convolutional Network (MS-TCN) architecture for sample-level activity prediction to joint segment and recognize the activity sequence. Furthermore, to enhance the robustness of HAR against the inter-class similarity and intra-class heterogeneity, a multi-level contrastive loss, containing the sample-level and segment-level contrast, has been proposed to learn a well-structured embedding space for better activity segmentation and recognition performance. Finally, with comprehensive experiments, we verify the effectiveness of the proposed method on two public HAR datasets, achieving significant improvements in the various evaluation metrics.
翻译:人类活动识别(HAR)具有磨损功能,是很有希望的研究,在许多智能保健应用中可以广泛采用。近年来,深深的学习型HAR模型取得了令人印象深刻的认知性业绩。然而,大多数HAR算法都容易遇到重要但很少被利用的多层窗口问题。在本文件中,我们提议通过将分解技术引入HAR,从而产生联合活动分解和识别。特别是,我们引入了多层时空网络(MS-TCN)样本级活动预测架构,以联合部分并承认活动顺序。此外,为了加强HAR的稳健性,防止阶级间相似性和阶级内差异性,一种包含抽样和分层差异的多层次对比性损失,我们建议通过学习结构完善的嵌入空间,以更好地进行活动分解和识别性。最后,通过全面实验,我们核查了两个公共法集的拟议方法的有效性,使各种评价指标得到显著改进。