  Human Activity Recognition (HAR) is considered a valuable research topic in
the last few decades. Different types of machine learning models are used for
this purpose, and this is a part of analyzing human behavior through machines.
It is not a trivial task to analyze the data from wearable sensors for complex
and high dimensions. Nowadays, researchers mostly use smartphones or smart home
sensors to capture these data. In our paper, we analyze these data using
machine learning models to recognize human activities, which are now widely
used for many purposes such as physical and mental health monitoring. We apply
different machine learning models and compare performances. We use Logistic
Regression (LR) as the benchmark model for its simplicity and excellent
performance on a dataset, and to compare, we take Decision Tree (DT), Support
Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).
Additionally, we select the best set of parameters for each model by grid
search. We use the HAR dataset from the UCI Machine Learning Repository as a
standard dataset to train and test the models. Throughout the analysis, we can
see that the Support Vector Machine performed (average accuracy 96.33%) far
better than the other methods. We also prove that the results are statistically
significant by employing statistical significance test methods.
