In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets (i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68 ). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.
@cite_22: Kernels in the convolutional layers of deep convolutional networks are believed to act as feature extractors, progressively highlighting more domain-specific features in the upper network layers. Thus lower-level features might be suitable for transfer. We analyse this in wearable activity recognition by reusing kernels learned on a source domain on another target domain. We consider transfer between users, application domains, sensor modalities and sensor locations. We characterize the trade-offs of transferring various convolutional layers along model size, learning speed, recognition performance and training data. Through novel kernel visualisations and comparative evaluations we identify what kernels are predominantly sensitive to, amongst sensor characteristics, motion dynamics and on-body placement. Kernel transfer reduces training time by 17 without additional complexity. We derive recommendations on when transfer is most suitable.
@cite_3: Abstract Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.
@cite_8: 
Deep learning based HAR @cite_3 achieves the state-of-the-art performance than conventional machine learning approaches. The reason is that deep learning is capable of automatically extracting high-level features from the raw sensor readings @cite_8 . Therefore, the features are likely to be more domain-invariant and tend to perform better for cross-domain tasks. A recent work evaluated deep models for cross-domain HAR @cite_22 , which provides some experience for future research on this area. There are stll many open problems for deep learning based CDAR. In this paper, we mainly focus on the traditional approaches.