Human Activity Recognition using wearable body sensor by machine learning approach

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliografiske detaljer
Main Authors:	Promi, Sadia Tangim, Rahman, Md. Zahidur, Mostafa, Moumita, Harun, Sarah Bintay
Andre forfattere:	Alam, Md. Golam Rabiul
Format:	Thesis
Sprog:	en_US
Udgivet:	Brac University 2020
Fag:	Human Activity Recognition HAR Machine Learning Convolutional Neural Network
Online adgang:	http://hdl.handle.net/10361/14054

id	10361-14054
record_format	dspace
spelling	10361-140542022-01-26T10:04:54Z Human Activity Recognition using wearable body sensor by machine learning approach Promi, Sadia Tangim Rahman, Md. Zahidur Mostafa, Moumita Harun, Sarah Bintay Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Human Activity Recognition HAR Machine Learning Convolutional Neural Network This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 48-51). The prevalence of electronics devices and the increase in computer resources, like networking, storage, accessibility and sensor capacity, have significantly improved the lives of humans. Now a days most smart devices have a number of strong sensing equipment, such as sensors for movement, position, connection and direction.Basically, movement or motion tracking sensors are commonly been using to classify the physical activities of humans. This has opened entryways for a wide range of and intriguing applications with regards to a numerous zones, for example, human healthcare well being and transportation, security system. In this point of view, this research gives a complete, best in class audit of the present circumstance of human activity recognition (HAR) approaches with regards to inertial sensors in electronic portable smartphone devices. Our research started by analyzing the principles of human activities and the entire historical events based on electronics deices such a smartphone, which demonstrate the development in this area over the past few years. Our approach concentrates on the introduction of the means of HAR arrangements with regards to sensors. We propose a methodology which incorporates traditional signal processing techniques with deep learning tools to robustly classify activities from wearable body sensor data. Our proposed methodology achieves a validation accuracy of 96.26% in the WISDM Dataset and is able to recognize human activity from wearable body sensor data robustly. Sadia Tangim Promi Md. Zahidur Rahman Moumita Mostafa Sarah Bintay Harun B. Computer Science 2020-10-11T05:45:24Z 2020-10-11T05:45:24Z 2019 2019-12 Thesis ID: 15301017 ID: 15101122 ID: 15201023 ID: 14101067 http://hdl.handle.net/10361/14054 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 51 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Human Activity Recognition HAR Machine Learning Convolutional Neural Network
spellingShingle	Human Activity Recognition HAR Machine Learning Convolutional Neural Network Promi, Sadia Tangim Rahman, Md. Zahidur Mostafa, Moumita Harun, Sarah Bintay Human Activity Recognition using wearable body sensor by machine learning approach
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Promi, Sadia Tangim Rahman, Md. Zahidur Mostafa, Moumita Harun, Sarah Bintay
format	Thesis
author	Promi, Sadia Tangim Rahman, Md. Zahidur Mostafa, Moumita Harun, Sarah Bintay
author_sort	Promi, Sadia Tangim
title	Human Activity Recognition using wearable body sensor by machine learning approach
title_short	Human Activity Recognition using wearable body sensor by machine learning approach
title_full	Human Activity Recognition using wearable body sensor by machine learning approach
title_fullStr	Human Activity Recognition using wearable body sensor by machine learning approach
title_full_unstemmed	Human Activity Recognition using wearable body sensor by machine learning approach
title_sort	human activity recognition using wearable body sensor by machine learning approach
publisher	Brac University
publishDate	2020
url	http://hdl.handle.net/10361/14054
work_keys_str_mv	AT promisadiatangim humanactivityrecognitionusingwearablebodysensorbymachinelearningapproach AT rahmanmdzahidur humanactivityrecognitionusingwearablebodysensorbymachinelearningapproach AT mostafamoumita humanactivityrecognitionusingwearablebodysensorbymachinelearningapproach AT harunsarahbintay humanactivityrecognitionusingwearablebodysensorbymachinelearningapproach
_version_	1814306989602242560

Human Activity Recognition using wearable body sensor by machine learning approach

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