  As a fundamental problem in ubiquitous computing and machine learning,
sensor-based human activity recognition (HAR) has drawn extensive attention and
made great progress in recent years. HAR aims to recognize human activities
based on the availability of rich time-series data collected from multi-modal
sensors such as accelerometers and gyroscopes. However, recent deep learning
methods are focusing on one view of the data, i.e., the temporal view, while
shallow methods tend to utilize the hand-craft features for recognition, e.g.,
the statistics view. In this paper, to extract a better feature for advancing
the performance, we propose a novel method, namely multi-view fusion
transformer (MVFT) along with a novel attention mechanism. First, MVFT encodes
three views of information, i.e., the temporal, frequent, and statistical views
to generate multi-view features. Second, the novel attention mechanism uncovers
inner- and cross-view clues to catalyze mutual interactions between three views
for detailed relation modeling. Moreover, extensive experiments on two datasets
illustrate the superiority of our methods over several state-of-the-art
methods.
