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dc.contributor.advisorRabiul Alam, Dr. Md. Golam
dc.contributor.authorAbedin, Minhajul
dc.contributor.authorAhad, Mohammad Abdul
dc.contributor.authorHasan-Ul-Banna, A.B.M
dc.contributor.authorKhan, Nibraz
dc.contributor.authorHossain, Ashfaq
dc.date.accessioned2023-03-28T06:40:01Z
dc.date.available2023-03-28T06:40:01Z
dc.date.copyright2022
dc.date.issued2022-09
dc.identifier.otherID: 18301224
dc.identifier.otherID: 18301248
dc.identifier.otherID: 18301143
dc.identifier.otherID: 18201057
dc.identifier.otherID: 18101658
dc.identifier.urihttp://hdl.handle.net/10361/18028
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 31-32).
dc.description.abstractAlzheimer’s is a brain disorder that gradually deteriorates the brain functions of the patients. As the disease progresses, victims start to lose their memory, thinking ability, eventually rendering them unable to perform basic tasks. They also face many difficulties namely disorientation, wandering, aggression, insomnia, hallucination, etc. What makes the situation worse is that when the caregivers try to help them most of the time they tend not to cooperate. In this paper, we have designed an AI that assists the sufferers in combating these issues by analyzing their environment, daily routine, interests, behavioral patterns, and many more factors. Using computer vision we have created a face recognition framework that identifies individuals in front of the patient & shows him/her their name, how they are related, and some photos & videos of them together. We also used an object detection system that helps prevent wandering by constantly monitoring the surroundings of the patient & notifying the caretakers about items such as keys, shoes, handbags, doors etc that could influence the patient to leave the house. The AI is instructed to alarm the attendant continuously if the patient somehow succeeds to go beyond the safe area. This feature allows the caregivers some free time as they don’t need to monitor the patients 24/7 anymore. The face recognition framework achieves accuracy of 97.44% and the object detection system has mAP of 72.3% that uses YOLOv7 model. Thus, this study tries to achieve its goal to make life comparatively easier for the patients & the caregivers by making the patients self-dependent & discharging the attendants from some of their tasks.en_US
dc.description.statementofresponsibilityMinhajul Abedin
dc.description.statementofresponsibilityMohammad Abdul Ahad
dc.description.statementofresponsibilityA.B.M Hasan-Ul-Banna
dc.description.statementofresponsibilityNibraz Khan
dc.description.statementofresponsibilityAshfaq Hossain
dc.format.extent32 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectAlzheimer’s Diseaseen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDeep Learningen_US
dc.subjectObject Detectionen_US
dc.subjectFace Recognitionen_US
dc.subjectFace Detectionen_US
dc.subjectFace Embeddingen_US
dc.subjectFace Classificationen_US
dc.subjectYOLOv4en_US
dc.subjectYOLOv7en_US
dc.subjectMTCNNen_US
dc.subjectFaceNeten_US
dc.subjectSVCen_US
dc.subjectRFsen_US
dc.subject.lcshAlzheimer’s disease
dc.titleAn ambient assisted living system for Alzheimer’s patientsen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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