Forecast of Human Activity Recognition based on Smart phones using Machine Learning

Abstract

Human Activity Recognition database was built from the recording of 30 study participants performing activities of daily living (ADL) while caring a waist mounted smart phones with embedded inertial sensors. The objective is to classify activities into one of the six activities performed. The experiments have been carried out with a group of 30 volunteers within an age bracket 19-48. Each person six activities wearing a smart phone on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly portioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data. The sensor signals (accelerometer & gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/ window).