Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have made use of such models to recognise human activities in a sensor-based manner, which have achieved good performance. However, sensor-based HAR still faces challenges; in particular, recognising similar activities that only have a different sequentiality and similarly classifying activities with large inter-personal variability. This means that some human activities have large intra-class scatter and small inter-class separation. To deal with this problem, we introduce a margin mechanism to enhance the discriminative power of deep learning networks. We modified four kinds of common neural networks with our margin mechanism to test the effectiveness of our proposed method. The experimental results demonstrate that the margin-based models outperform the unmodified models on the OPPORTUNITY, UniMiB-SHAR, and PAMAP2 datasets. We also extend our research to the problem of open-set human activity recognition and evaluate the proposed method's performance in recognising new human activities.

中文翻译：

---

用于人类活动识别的基于裕度的深度学习网络。

人类活动识别（HAR）是一个流行且具有挑战性的研究主题，由各种应用驱动。最近，随着用于分类任务的深度学习网络的发展取得了重大进展，许多研究人员利用此类模型以基于传感器的方式识别人类活动，并取得了良好的性能。然而，基于传感器的HAR仍然面临挑战；特别是，识别仅具有不同顺序的相似活动，并对具有较大人际差异的活动进行类似分类。这意味着某些人类活动具有较大的类内分散性和较小的类间分离性。为了解决这个问题，我们引入了边际机制来增强深度学习网络的判别能力。我们用边缘机制修改了四种常见的神经网络，以测试我们提出的方法的有效性。实验结果表明，基于保证金的模型在 OPPORTUNITY、UniMiB-SHAR 和 PAMAP2 数据集上优于未修改的模型。我们还将我们的研究扩展到开放集人类活动识别问题，并评估所提出的方法在识别新人类活动方面的性能。