Abstract: Human activity recognition (HAR) is a crucial task in the field of physical activity monitoring. It involves the identification of a person's activities and movements using sensors. The accuracy of HAR systems plays a significant role in enhancing physical health, preventing accidents and injuries, and improving security systems. One of the most common approaches to HAR is using smartphones as sensors. However, smartphones have limited processing power and battery life, which can impact the performance of HAR systems. To overcome these challenges, we can apply different machine learning models and compare their performances. We begin by selecting a standard dataset, the HAR dataset from the Machine Learning Repository. We then apply several Machine learning models, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).To compare the performance of these models, we train and test each model on the HAR dataset. We also select the best set of parameters for each model using grid search. Our results show that the Support Vector Machine (SVM) performed the best (average accuracy 96.33%), significantly outperforming the other models. We can confirm the statistical significance of these results by employing statistical significance test methods.The SVM model demonstrated superior performance in recognizing human activities. This highlights the potential of machine learning models in revolutionizing the field of HAR. However, further research is needed to address the challenges of limited processing power and battery life in smartphones and to explore other potential applications of HAR systems.