The sensor-based human activity recognition (HAR) using machine learning requires a sufficiently large amount of annotated data to realize an accurate classification model. This requirement stimulates the advancement of the transfer learning research area that minimizes the use of labeled data by transferring knowledge from the existing activity recognition domain. Existing approaches transform the data into a common subspace between domains which theoretically loses information, to begin with. Besides, they are based on the linear projection which is bound to linearity assumption and its limitations. Some recent works have already incorporated nonlinearity to find a latent representation that minimizes domain discrepancy based on an autoencoder that includes statistical distance minimization. However, such approach discovers latent representation for both domains at once, which causes sub-optimal representation because both domains compensate each other’s reconstruction error during the training. We propose an autoencoder-based approach on domain adaptation for sensor-based HAR. The proposed approach learns a latent representation which minimizes the discrepancy between domains by reducing statistical distance. Instead of learning representation of both domains simultaneously, our method is a two-phase approach which first learns the representation for the domain of interest independently to ensure its optimality. Subsequently, the useful information from the existing domain is transferred. We test our approach on the publicly available sensor-based HAR datasets, using cross-domain setup. The experimental result shows that our approach significantly outperforms the existing ones.

使用机器学习的基于传感器的人类活动识别（HAR）需要足够大量的注释数据，以实现准确的分类模型。这项要求刺激了迁移学习研究领域的发展，该领域通过从现有活动识别领域中转移知识来最大程度地减少了标记数据的使用。首先，现有方法将数据转换为域之间的公共子空间，从理论上讲，这些子空间理论上会丢失信息。此外，它们基于线性投影，该线性投影受线性假设及其局限性的约束。最近的一些工作已经结合了非线性，以基于包括统计距离最小化的自动编码器来找到将域差异最小化的潜在表示。然而，这种方法会立即发现两个域的潜在表示，这会导致次优表示，因为两个域在训练过程中会相互补偿对方的重构误差。我们提出了一种基于自动编码器的域自适应方法，用于基于传感器的HAR。所提出的方法学习一种潜在表示，该潜在表示通过减小统计距离来最小化域之间的差异。不同于同时学习两个域的表示，我们的方法是一种两阶段方法，该方法首先独立学习感兴趣域的表示以确保其最优性。随后，将传输来自现有域的有用信息。我们使用跨域设置在公开可用的基于传感器的HAR数据集上测试了我们的方法。