  Human Activity Recognition (HAR) using deep neural network has become a hot
topic in human-computer interaction. Machine can effectively identify human
naturalistic activities by learning from a large collection of sensor data.
Activity recognition is not only an interesting research problem, but also has
many real-world practical applications. Based on the success of residual
networks in achieving a high level of aesthetic representation of the automatic
learning, we propose a novel \textbf{A}symmetric \textbf{R}esidual
\textbf{N}etwork, named ARN. ARN is implemented using two identical path
frameworks consisting of (1) a short time window, which is used to capture
spatial features, and (2) a long time window, which is used to capture fine
temporal features. The long time window path can be made very lightweight by
reducing its channel capacity, yet still being able to learn useful temporal
representations for activity recognition. In this paper, we mainly focus on
proposing a new model to improve the accuracy of HAR. In order to demonstrate
the effectiveness of ARN model, we carried out extensive experiments on
benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared with some
conventional and state-of-the-art learning-based methods. Then, we discuss the
influence of networks parameters on performance to provide insights about its
optimization. Results from our experiments show that ARN is effective in
recognizing human activities via wearable datasets.
