  A person's movement or relative positioning can be effectively captured by
different types of sensors and corresponding sensor output can be utilized in
various manipulative techniques for the classification of different human
activities. This letter proposes an effective scheme for human activity
recognition, which introduces two unique approaches within a multi-structural
architecture, named FusionActNet. The first approach aims to capture the static
and dynamic behavior of a particular action by using two dedicated residual
networks and the second approach facilitates the final decision-making process
by introducing a guidance module. A two-stage training process is designed
where at the first stage, residual networks are pre-trained separately by using
static (where the human body is immobile) and dynamic (involving movement of
the human body) data. In the next stage, the guidance module along with the
pre-trained static or dynamic models are used to train the given sensor data.
Here the guidance module learns to emphasize the most relevant prediction
vector obtained from the static or dynamic models, which helps to effectively
classify different human activities. The proposed scheme is evaluated using two
benchmark datasets and compared with state-of-the-art methods. The results
clearly demonstrate that our method outperforms existing approaches in terms of
accuracy, precision, recall, and F1 score, achieving 97.35% and 95.35% accuracy
on the UCI HAR and Motion-Sense datasets, respectively which highlights both
the effectiveness and stability of the proposed scheme.
