A Novel Attention-Based Convolution Neural Network for Human Activity Recognition

Human Activity Recognition (HAR) has been widely used for various applications, such as smart homes, healthcare, security and human-robot interaction. In this paper, a novel deep learning model is proposed based on different Convolution Neural Networks (CNN) and the attention mechanism for addressing the HAR problem by using sensory data from accelerometers and gyroscopes and providing high recognition accuracy. The architecture of the proposed model (named LGSTNet) is designed to be able to analyse and extract Local and Global Spatial-Temporal features from sensory data for HAR. The idea of LGSTNet is to segment an activity window into several sub-windows and join the Attention mechanism into an 2D CNN (named Attention-based 2D-CNN module) to precisely learn local spatial-temporal features from those sub-windows. Meanwhile, an 3D CNN (named 3D-CNN module) is designed to learn global spatial-temporal features from the whole window. Both comparison and ablation experiments are conducted on two real-world datasets, named
WISDM
and
UCI-HAR
. By comparing LGSTNet to eight other well-known existing models and its variants on both datasets, LGSTNet achieves the best performance (over 0.8064 and
$0.9569\,\,{F}_{w}-\textit {Meansure}$
on
WISDM
and
UCI-HAR
, respectively) and is also more robust.