Prediction of human activity using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| On-line přístup: | http://hdl.handle.net/10361/14055 |
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10361-140552022-01-26T10:13:18Z Prediction of human activity using machine learning Tisha, Sadia Nasrin Alvee, Benjir Islam Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Machine Learning HAR Human Activity Recognition 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 79-83). Involving machine learning in recognizing human activities is a widely discussed topic of this era. It has a noticeable growth of interest for implementing a wide range of applications such as health monitoring, indoor movements, navigation and location-based services. The process is implemented gradually through several methods obtaining better accuracy than before. The data of human activities can be collected by wifi module, bioharness or wearable device which can be waist, wrist or thighs mounted. The purpose of our research is predicting human activities by classifying sequences of remotely recorded data of well-defined human movements using responsive sensors. The data are collected by a waist mounted device which contains mobile phone sensors (e.g. accelerometer and gyroscope) for observing human activities of different aged people. The observed data are modeled using machine learning and neural network. Here we have used machine learning algorithms which are Support Vector Machine (SVM), K Nearest Neighbour (KNN), Linear Regression, Logistic Regression, Decision Tree, Naive Bayes Classifier and Random Forest Classifier. Moreover, we have also used artificial recurrent neural network (RNN) architecture- Long Short-Term Memory algorithm and Multi Layer Perceptron (MLP) algorithm. Modeling the data using various algorithms and obtaining results accurately are not convenient, because human motions recorded through wearable sensors have variations and complexity. For overcoming these problems we have used four dimension reduction techniques e.g. Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) for achieving more accurate activity prediction performance with less complex and faster computations. Sadia Nasrin Tisha Benjir Islam Alvee B. Computer Science 2020-10-12T05:32:29Z 2020-10-12T05:32:29Z 2019 2019-12 Thesis ID: 16101101 ID: 16101112 http://hdl.handle.net/10361/14055 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. 83 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Machine Learning HAR Human Activity Recognition |
| spellingShingle |
Machine Learning HAR Human Activity Recognition Tisha, Sadia Nasrin Alvee, Benjir Islam Prediction of human activity using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Tisha, Sadia Nasrin Alvee, Benjir Islam |
| format |
Thesis |
| author |
Tisha, Sadia Nasrin Alvee, Benjir Islam |
| author_sort |
Tisha, Sadia Nasrin |
| title |
Prediction of human activity using machine learning |
| title_short |
Prediction of human activity using machine learning |
| title_full |
Prediction of human activity using machine learning |
| title_fullStr |
Prediction of human activity using machine learning |
| title_full_unstemmed |
Prediction of human activity using machine learning |
| title_sort |
prediction of human activity using machine learning |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/14055 |
| work_keys_str_mv |
AT tishasadianasrin predictionofhumanactivityusingmachinelearning AT alveebenjirislam predictionofhumanactivityusingmachinelearning |
| _version_ |
1814308137996386304 |