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.

本文介绍了一种基于深度学习的多类窗口问题解决方案 —— 使用分割技术的人类活动识别方法，并引入多阶段时间卷积网络（MS-TCN）进行样本级别的活动预测，同时使用多级对比损失以提高方法的鲁棒性，并在两个公共 HAR 数据集上验证了该方法的有效性。