Performance Evaluation of Ensemble Learners on Smartphone Sensor Generated Human Activity Data Set

Abstract

Nowadays, mobile phone sensor technology is advancing at a great pace and is consequently used to perform several tasks with the preinstalled GPS and accelerometer sensors on it. Different human activities including jogging, sitting, standing, walking, climbing stairs, etc., are automatically recorded on a cell phone with the help of installed sensors. The stored data is used to recognize human activities using various machine learning algorithms. In this paper, the performance evaluation of Ensemble learner on the raw sensor data is performed. The WISDM dataset, which uses a phone-based accelerometer to recognize the activities of a human, is used for experimentation. The used dataset keeps a record of the basic human activities such as jogging, sitting, standing, walking, climbing up and down stairs, in the day to day life. The WISDM data contains some missing values which are replaced using the average values. The ensemble classifiers including random forest, AdaBoost, and bagging are used for human activity classification. The performance of classifiers is evaluated against the parameters of accuracy, recall, precision, and f1-measures. The experimentation suggests that the random forest classifier outperforms the other two on every parameter value.