Wearable sensors of a smartphone are often used in Human activity recognition (HAR) application. Recently, utilization of wearable sensors has raised more importance in several applications such as computer games, medical care animation and film making. In this paper, classification of HAR using different type of Ensemble methods has acquired database from 30 subjects using smartphone to perform six different daily activities. The signal processing stages involved filtering and segmentation, time domain and frequency domain of feature extraction and classification for walking, walking upstairs, walking downstairs, sitting, standing and laying. The classification involved ensemble methods of Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace with Random Forest(RF) as base classifier. Holdout method and 10 fold cross validation method are executed to evaluate the classification of each activity. Each activity assessment was based on precision, recall, F-measure and overall accuracy. From the results of classification of all activities, it was found that Adaboost-RF classifier given overall accuracy rate 98.07 for holdout method and 98.82 10 fold cross validation method. © 2018 IEEE.