A Deep Learning Framework for Human Activity Recognition Using Smartphone Data

Abstract: Smart phones are the most helpful apparatuses of our day-by-day life and with the propelling innovation; they get fit systematically to address user’s issues and desires. To make these contraptions more useful and amazing, originators add new modules and gadgets to the equipment. Sensors has a major part in making cell phones more practical and mindful of the climate subsequently most smart phones accompany distinctive inserted sensors and this makes it conceivable to gather tremendous measures of data about the client's day by day life and exercises. The goal of Human Activity Recognition is to identify the activities performed by an individual from a given set of the information about him/her and his general environment. A great deal of exploration is being done in the field of Human Activity Recognition which human conduct is deciphered by reasoning highlights got from development, place, physiological signs and data from environments. The propose system presents a deep learning framework for human activity recognition using a smartphone data. The dataset was downloaded from kaggle.com. The dataset contains accelerometer and gyroscope data gotten from a Samsung Galaxy S2 smartphone. The accelerometer and gyroscope data is made up of different activities performed by an individual. The propose system uses keras framework and Theano as backend in build our model. After successful training, the proposed method had an accuracy of 99.06% on the 120th epoch. Keyword: Deep Learning Algorithm, Theano, Keras, Smartphone, Activities Recognition