  Inertial Measurement Unit (IMU) sensors are widely employed for Human
Activity Recognition (HAR) due to their portability, energy efficiency, and
growing research interest. However, a significant challenge for IMU-HAR models
is achieving robust generalization performance across diverse users. This
limitation stems from substantial variations in data distribution among
individual users. One primary reason for this distribution disparity lies in
the representation of IMU sensor data in the local coordinate system, which is
susceptible to subtle user variations during IMU wearing. To address this
issue, we propose a novel approach that extracts a global view representation
based on the characteristics of IMU data, effectively alleviating the data
distribution discrepancies induced by wearing styles. To validate the efficacy
of the global view representation, we fed both global and local view data into
model for experiments. The results demonstrate that global view data
significantly outperforms local view data in cross-user experiments.
Furthermore, we propose a Multi-view Supervised Network (MVFNet) based on
Shuffling to effectively fuse local view and global view data. It supervises
the feature extraction of each view through view division and view shuffling,
so as to avoid the model ignoring important features as much as possible.
Extensive experiments conducted on OPPORTUNITY and PAMAP2 datasets demonstrate
that the proposed algorithm outperforms the current state-of-the-art methods in
cross-user HAR.
