Human Activity Recognition using Deep Neural Network

The smartphone has become quite ubiquitous and an indispensable part of our lives in the modern day. It has many sensors which capture several minute details pertaining to our activities. So, it is but inevitable that human desire creeps in to augment and improve one's own actions by studying such behaviour captured through the instrumentalities of the smart-phone. In this context, study of data on human activities captured through accelerometer and gyroscope get primal significance. In this paper, we have attempted to apply machine learning and deep learning techniques on a publicly available dataset. Initially, classification algorithms like K-Nearest Neighbours and Random Forest are applied. The classification accuracies observed are 90.46% and 92.97% respectively. Using benchmark feature selection and dimensionality reduction techniques does not improve the model accuracies to a large extent - with reported accuracies of 91.48% and 92.56% respectively. However, on employing deep neural network techniques, an accuracy of 97.32% is achieved, which indicates suitability of deep learning techniques over traditional machine learning techniques for the task of human activity recognition using mobile sensor data.