This paper focuses on accuracy improvement of human activities detection and classification by using single Inertia Measurement Unit sensor (IMU sensor: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) which is a type of the wearable sensors. Generally, performance of classification model is determined by these methodologies; number and type of sensors, coordinate transformation, time window, time-frequency domain analysis, and machine learning algorithms. The contributions of this paper are summarized in the following three points. Firstly, a pressure sensor is additionally utilized to improve the accuracy of human activities estimation. This information is effective to estimate up/down motion by stair and elevator. Secondly, comprehensive evaluation of the combinations using different methodologies is conducted to find an optimal classification model. Thirdly, ensemble learning is performed to improve estimation accuracy. It shows superior performance with over 95 % accuracy of human activity estimation.