Activity monitoring system using deep learning for people with dementia
| dc.contributor.author | Qadeer, Amarzish | |
| dc.contributor.examiningcommittee | Siddiqui, Tabrez (Physiology and Pathophysiology) | |
| dc.contributor.examiningcommittee | Sherif, Sherif (Electrical and Computer Engineering) | |
| dc.contributor.supervisor | Choukou, Amine | |
| dc.date.accessioned | 2023-06-22T16:31:32Z | |
| dc.date.available | 2023-06-22T16:31:32Z | |
| dc.date.copyright | 2023-05-09 | |
| dc.date.issued | 2023-05-09 | |
| dc.date.submitted | 2023-05-09T19:37:18Z | en_US |
| dc.degree.discipline | Biomedical Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Dementia is a degenerative condition that affects cognitive abilities and daily functioning. This project aims to explore and evaluate activity recognition algorithms to support the assisted living of people with dementia. The proposed deep learning approach can help to monitor people with dementia and support their caregivers in providing effective care. We tried a new approach for detecting the activities of daily living for people with dementia. We explored ExpansionNet_v2 model and used it to train on the Toyota Smart Home dataset in order to detect the activities od daily living. The dataset was converted into COCO dataset format. Bounding boxes were generated using Faster-RCNN with ResNet backbone pretrained model from pytorch. Captions were generated using scene understanding. This involved analyzing the image or video to extract semantic information about the environment and objects within it, including their relationships and context. Semantic relationships and patterns were extracted, which helped in building a more comprehensive understanding of the scene. The training process involved two steps - initial training and fine-tuning. During initial training, newly added layers were trained while keeping the pre-trained layers of the Swin-Transformer backbone frozen. Fine-tuning involved training the entire network, including both the pre-trained backbone and newly added layers, on the dataset. The purpose of using multiple frames from a video during training is to increase the probability of detecting the pose accurately and generating a good caption. The algorithm to detect ADL was tested on real-life videos of three dementia patients at different stages of dementia. The daily activities of these patients were recorded to test the algorithm after training and validation on the Toyota SmartHome dataset. | en_US |
| dc.description.note | October 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37387 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Dementia | en_US |
| dc.subject | Activities of Daily Living | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | Monitoring | en_US |
| dc.subject | Older adults | en_US |
| dc.title | Activity monitoring system using deep learning for people with dementia | |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no | en_US |