  Human Activity Recognition (HAR) using wearable devices such as smart watches
embedded with Inertial Measurement Unit (IMU) sensors has various applications
relevant to our daily life, such as workout tracking and health monitoring. In
this paper, we propose a novel attention-based approach to human activity
recognition using multiple IMU sensors worn at different body locations.
Firstly, a sensor-wise feature extraction module is designed to extract the
most discriminative features from individual sensors with Convolutional Neural
Networks (CNNs). Secondly, an attention-based fusion mechanism is developed to
learn the importance of sensors at different body locations and to generate an
attentive feature representation. Finally, an inter-sensor feature extraction
module is applied to learn the inter-sensor correlations, which are connected
to a classifier to output the predicted classes of activities. The proposed
approach is evaluated using five public datasets and it outperforms
state-of-the-art methods on a wide variety of activity categories.
