Human Activity Recognition With Smartphone

Abstract

Abstract: Human Activity Recognition (HAR) is designed to classify activities performed by human beings using responsive sensors inbuilt in smartphones that are affected from their movements. HAR is one of the most important technology that has wide applications in medical research, human survey system and security system, fitness etc. In this project we are predicting what a person is doing based on their trace movement using smartphone sensor. Movements include normal indoor human physical activities such as standing, sitting, walking, running, walking upstairs, walking downstairs. Smartphones has various built-in sensors like accelerometer and gyroscope. This system captures the raw sensor data from mobile sensors as input, process it and predicts a human activity using machine learning techniques. We analyze the performance of two classification algorithms that is Decision Tree (DT), Convolutional Neural Network (CNN). In this project, we build a robust activity detection system based on smartphones. Experiment results show that the classification rate of algorithms reaches 98%. In this project we propose a platform for identifying real-time human activity. Keywords: Machine Learning, Deep Learning, Activity Recognition, Smartphone, Accelerometer, Gyroscope, CNN, DT.