Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
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10361-228892024-05-20T21:04:40Z Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach Islam, Md. Nazmul Al-Amin, Md. Hassan, Md. Zaed Islam, Tamzid Rhaman, Md. Khalilur Department of Computer Science and Engineering, Brac University Companion system Deep learning CNN Reinforced learning NLP Medication habit Natural language processing (Computer science) Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 58-60). The increasing number of elderly individuals living alone has emerged as a pressing global concern. Our research aims to address this issue by developing advanced modules that can be integrated into a system that enhances the quality of life for older adults. The modules focus on medicine detection, fall detection, reminders for important tasks and events and providing companionship through friendly verbal interactions. Through the integration of cutting-edge deep learning techniques, diverse models and natural language processing (NLP), we have successfully designed an effective medication and well-being assistant. These modules use computer vision technology along with reinforced learning from human feedback and convolutional neural networks (CNNs) to reach our goal. The modules can be integrated into systems to empower elderly individuals to lead more active and fulfilling lives. Finally, this research contributes to the well-being and happiness of the elderly, highlighting the significance of comprehensive support systems in promoting their overall well-being. Md. Nazmul Islam Md. Al-Amin Md. Zaed Hassan Tamzid Islam B.Sc in Computer Science and Engineering 2024-05-20T08:35:56Z 2024-05-20T08:35:56Z ©2024 2024-01 Thesis ID: 19301033 ID: 19301028 ID: 19301024 ID: 19301050 http://hdl.handle.net/10361/22889 en 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. 72 pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
language |
English |
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Companion system Deep learning CNN Reinforced learning NLP Medication habit Natural language processing (Computer science) Deep learning (Machine learning) |
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Companion system Deep learning CNN Reinforced learning NLP Medication habit Natural language processing (Computer science) Deep learning (Machine learning) Islam, Md. Nazmul Al-Amin, Md. Hassan, Md. Zaed Islam, Tamzid Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach |
description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. |
author2 |
Rhaman, Md. Khalilur |
author_facet |
Rhaman, Md. Khalilur Islam, Md. Nazmul Al-Amin, Md. Hassan, Md. Zaed Islam, Tamzid |
format |
Thesis |
author |
Islam, Md. Nazmul Al-Amin, Md. Hassan, Md. Zaed Islam, Tamzid |
author_sort |
Islam, Md. Nazmul |
title |
Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach |
title_short |
Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach |
title_full |
Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach |
title_fullStr |
Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach |
title_full_unstemmed |
Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach |
title_sort |
health trauma and well-being assistant for bengali seniors in household: a multimodal approach |
publisher |
Brac University |
publishDate |
2024 |
url |
http://hdl.handle.net/10361/22889 |
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