  Together with the rapid development of the Internet of Things (IoT), human
activity recognition (HAR) using wearable Inertial Measurement Units (IMUs)
becomes a promising technology for many research areas. Recently, deep
learning-based methods pave a new way of understanding and performing analysis
of the complex data in the HAR system. However, the performance of these
methods is mostly based on the quality and quantity of the collected data. In
this paper, we innovatively propose to build a large database based on virtual
IMUs and then address technical issues by introducing a multiple-domain deep
learning framework consisting of three technical parts. In the first part, we
propose to learn the single-frame human activity from the noisy IMU data with
hybrid convolutional neural networks (CNNs) in the semi-supervised form. For
the second part, the extracted data features are fused according to the
principle of uncertainty-aware consistency, which reduces the uncertainty by
weighting the importance of the features. The transfer learning is performed in
the last part based on the newly released Archive of Motion Capture as Surface
Shapes (AMASS) dataset, containing abundant synthetic human poses, which
enhances the variety and diversity of the training dataset and is beneficial
for the process of training and feature transfer in the proposed method. The
efficiency and effectiveness of the proposed method have been demonstrated in
the real deep inertial poser (DIP) dataset. The experimental results show that
the proposed methods can surprisingly converge within a few iterations and
outperform all competing methods.
