Abstract Machine learning-based human activity recognition (HAR) as the means of human–computer interaction is important to empower the existing systems in the industry such as manufacturing and logistics to be more autonomous. However, it is often difficult to build HAR models due to the limitation of annotated samples. Domain adaptation has emerged to address such limitation (or absence) of labeled samples in the domain of interest (i.e., target domain) by using abundant amount of labeled samples in the other domain (i.e., source domain). Domain adaptation aims to solve learning problems where a source domain and a target domain are different but still related. With the use of homogeneous feature space, the existing approaches on homogeneous domain adaption is prohibitive for the real-life scenario, in which two different domains could be possibly of heterogeneous feature space. Although heterogeneous domain adaptation approaches exist, it still requires additional information (i.e., instance correspondence) which is difficult to satisfy when we deal with sensor data. Hybrid domain adaptation, a special case of heterogeneous domain adaptation where common feature between domain exists, is more realistic as the feature commonality is easier to satisfy. However, the existing approach operates using common features in the original feature space, which in fact, may still have distribution difference. In addition, the existing one requires hand-crafted feature extractions for more informative descriptors to classify human activities. In this work, we propose a deep architecture for hybrid domain adaptation, to enable end-to-end learning for human activity classification. The architecture is tested on sensor-based human activity recognition dataset. The experimental result shows that our approach yields better result compared to the existing human activity recognition approaches, both the deep and shallow approaches.

中文翻译：

---

具有深度网络架构的混合域自适应，用于端到端跨域人类活动识别

摘要 基于机器学习的人类活动识别（HAR）作为人机交互的手段，对于使制造业和物流等行业现有系统更加自主非常重要。然而，由于注释样本的限制，通常很难构建 HAR 模型。通过使用其他域（即源域）中的大量标记样本，域适应已经出现以解决感兴趣域（即目标域）中标记样本的这种限制（或缺失）。域适应旨在解决源域和目标域不同但仍然相关的学习问题。通过使用同构特征空间，现有的同构域适应方法对于现实生活场景来说是令人望而却步的，其中两个不同的域可能是异构特征空间。尽管存在异构域自适应方法，但它仍然需要额外的信息（即实例对应），这在我们处理传感器数据时很难满足。混合域自适应是异构域自适应的一种特殊情况，其中域之间存在共同特征，因为特征共同性更容易满足，所以更现实。然而，现有方法使用原始特征空间中的公共特征进行操作，实际上可能仍然存在分布差异。此外，现有的需要手工特征提取以获得更多信息描述符来对人类活动进行分类。在这项工作中，我们提出了一种用于混合域适应的深层架构，实现人类活动分类的端到端学习。该架构在基于传感器的人类活动识别数据集上进行了测试。实验结果表明，与现有的人类活动识别方法（深层次和浅层次方法）相比，我们的方法产生了更好的结果。