In day-to-day existence, people act on many tasks. It is vital to record and analyze the daily presence of individual people. Hence it could assist with relieving a few medical conditions and different issues. Human Activity Recognizing is a key component of research topics in computer vision for different sectors like security monitoring, healthcare and human-computer association, and sports. Nowadays, the smartphone has become popular and helpful for people. Because smartphone has many various and effective sensors, in this paper, we have used smartphone sensors: an accelerometer and gyroscope to detect human activity. In our research, we collected 30 study participants labeled datasets between ages nine-teen to four-ty-eight (19-48) years who have executed actions such as activities of daily life including sitting, walking, standing, walking up or down stairs, and lying down while using a smartphone equipped with such sensors. The objective is to do each of the six activities in the correct order. Two sets of the recorded dataset were randomly chosen, with 70% of participants 30% chosen to produce test data, and the remaining 70% to produce training data. The results were gained along with compared by supervised classification algorithms such as Random Forest, decision trees, KNN method, Support vector machines, and Logistic Regression. By comparing those algorithms, we gained the best results accuracy from Logistic Regression which is 96.21%. © 2023 IEEE.