Human Activity Recognition with Smartphones Using Machine Learning

Abstract

Human activity recognition is widely used now in many applications, such as smart homes, health care, and business as well as in a wide range of pattern recognition and human-computer interaction research. In this paper, we use the same sensors embedded in smartphones (Accelerometer and Gyroscope) to track and recognize human activities.  We employ a machine-learning algorithm, which is the Support Vector Machine (SVM) to improve the performance of human activity recognition. The experimental results on the HAR Dataset from the UCI repository indicate that our approach is practical and achieves 96% accuracy.