  Human Activity Recognition (HAR) simply refers to the capacity of a machine
to perceive human actions. HAR is a prominent application of advanced Machine
Learning and Artificial Intelligence techniques that utilize computer vision to
understand the semantic meanings of heterogeneous human actions. This paper
describes a supervised learning method that can distinguish human actions based
on data collected from practical human movements. The primary challenge while
working with HAR is to overcome the difficulties that come with the
cyclostationary nature of the activity signals. This study proposes a HAR
classification model based on a two-channel Convolutional Neural Network (CNN)
that makes use of the frequency and power features of the collected human
action signals. The model was tested on the UCI HAR dataset, which resulted in
a 95.25% classification accuracy. This approach will help to conduct further
researches on the recognition of human activities based on their biomedical
signals.
