NORMA eResearch @NCI Library

A Deep Learning Framework for Memory Retrieval from Lifelogging Data

Aman, Mohammad (2022) A Deep Learning Framework for Memory Retrieval from Lifelogging Data. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (1MB) | Preview


An emerging trend known as lifelogging is a process of digitally documenting and processing the data of an individual’s daily experiences. Lifelogging creates data which can be noisy and with continuity; therefore, it is challenging to give a comprehensive means of retrieving events or moments of interest to the public. This research proposes a deep learning framework to improve memory retrieval from lifelogging data. The proposed framework combines text-image embeddings and ensembles of a zero-shot deep learning model. The framework is implemented using three versions of the Contrastive Language-Image Pre-training (CLIP) model based on the combination of 12 datasets created by seven users containing more than 100000 images. The results are evaluated based on the average precision@k metric for different values of k. This framework improves retrieval performance and shows the possibility of helping people who have Alzheimer’s and other forms of dementia to recall useful information using the retrieval framework.

Item Type: Thesis (Masters)
Stynes, Paul
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 May 2023 09:31
Last Modified: 17 May 2023 09:31

Actions (login required)

View Item View Item