Human Activity Recognition System Using Smartphone Data Sensors with Python and Machine Learning

Abstract

Abstract: This project depicts recognition Human activity Using data generated from user Smartphones Machine Learning repository to recognize six human activities. These activities are standing, sitting, laying, walking, upstair and walking, ddownstairs. Data is collected from embedded accelerometer, gyroscope and other sensor .Data is randomly divided into 7:3 ratios to From training and testing data set respectively. Activity Classification done using Machine Learning models Namely Random Forest. support vector machine, Neural Network and k-Nearest Neighbor. We have compared accuracy and performance of these model using confusion matrix and random simulation. Human Activity recognition(HAR) is classifying activity of person using responsive sensor that are affected from human movement. Both users and capabilities of smartphone With them. These facts makes HAR more important and Popular. This work focuses on recognition of Human activity using smartphone sensor different machine learning clssification approaches. Data retrieved from smartphones accelerometer and gyroscope sensor are classified On order to recognize human activity. Results of the approaches used compared in terms of efficiency and precision. Keywords: CNN, Accelerometer and gyroscope LSTM Model, Machine Learning, SVM etc