Human Activity Recognition (HAR) is a feature
of an automated system that recognizes human actions. Since
most people these days are health-conscious, people use their
smartphones or smartwatches to track their daily activities. This
helps them organize their schedules and lifestyles more effectively.
Recent advancements in Deep Learning (DL) performance have
mitigated certain issues related to HAR. Consequently, DL methods
are essential for improved competence and precision. This
paper provides a comparative study that utilizes state-of-the-art
Kolmogorov-Arnold Network (KAN) and Multi-layer Perceptron
(MLP) to classify human activities using biometrics data. The
Biometrics dataset, which includes 18 classes representing a
variety of activities, is used for HAR. For optimal outcomes, the
suggested algorithm is trained and tested using the TensorFlow
structure and a hyperparameter tuning technique. The outcomes
show that the KAN algorithm performs quite well in identifying
human activity with an accuracy of 72.64% and a loss rate of
0.9136. The experiment’s findings suggested that the KAN model
performs more effectively and accurately for human activity
identification.