  The popularity and diffusion of wearable devices provides new opportunities
for sensor-based human activity recognition that leverages deep learning-based
algorithms. Although impressive advances have been made, two major challenges
remain. First, sensor data is often incomplete or noisy due to sensor placement
and other issues as well as data transmission failure, calling for imputation
of missing values, which also introduces noise. Second, human activity has
multi-scale characteristics. Thus, different groups of people and even the same
person may behave differently under different circumstances. To address these
challenges, we propose a multilevel heterogeneous neural network, called MHNN,
for sensor data analysis. We utilize multilevel discrete wavelet decomposition
to extract multi-resolution features from sensor data. This enables
distinguishing signals with different frequencies, thereby suppressing noise.
As the components resulting from the decomposition are heterogeneous, we equip
the proposed model with heterogeneous feature extractors that enable the
learning of multi-scale features. Due to the complementarity of these features,
we also include a cross aggregation module for enhancing their interactions. An
experimental study using seven publicly available datasets offers evidence that
MHNN can outperform other cutting-edge models and offers evidence of robustness
to missing values and noise. An ablation study confirms the importance of each
module.
