A two-phase human activity classification design using accelerometer data from smartphone

Abstract

This paper presents a two-phase design for the classification of the human movement activities of running, walking and standing by using accelerometer data logged from smartphone. The first phase of the design is, the two-stage filter applied for the process of raw acceleration data collected from the accelerometer; and the second phase of the design is, the classification of human movements using Logistic Regression with Gradient Descent Algorithm as classifier from the clean data produced by first phase of the design. The raw acceleration data from the tri-axial accelerometer of smartphone contains three axes accelerations. This acceleration data are collected separately for each movement activity and are used for the training and testing of design by using a simulation environment. The activity classification results are promising and show that the proposed design provides an overall accuracy of more than 97% on both train data and the separate independent test data for the classification of human movement activities of running, walking and standing.