An ambient assisted living system for Alzheimer’s patients

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.issued2022-09
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 31-32).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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.
dc.identifier.otherID: 18301224
dc.identifier.otherID: 18301248
dc.identifier.otherID: 18301143
dc.identifier.otherID: 18201057
dc.identifier.otherID: 18101658
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/9eb608a9-7317-452e-897f-bac12839b68b
dc.identifier.urihttp://hdl.handle.net/10361/18028
dc.language.isoen
dc.publisherBRAC University
dc.sourceBRAC University Institutional Repository
dc.subjectAlzheimer’s Disease
dc.subjectArtificial Intelligence
dc.subjectDeep Learning
dc.subjectObject Detection
dc.subjectFace Recognition
dc.subjectFace Detection
dc.subjectFace Embedding
dc.subjectFace Classification
dc.subjectYOLOv4
dc.subjectYOLOv7
dc.subjectMTCNN
dc.subjectFaceNet
dc.subjectSVC
dc.subjectRFs
dc.titleAn ambient assisted living system for Alzheimer’s patients
dc.typeThesis

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