Citation

Salehi, Parsa and Chua, Sook Ling and Foo, Lee Kien (2024) Annotating Activity Data with Transformer-based Deep Learning Model. In: IMMS '24: Proceedings of the 2024 7th International Conference on Information Management and Management Science, August 23 - 25, 2024, Beijing China.

Text Annotating Activity Data with Transformer-based Deep Learning Model.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

There are many activity recognition systems based on supervised learning that have been proposed over the years. One problem with supervised learning is that it requires sufficient number of labelled data for training. The majority of the labelling tasks are done manually by the user themselves. This process is rather time-consuming and tedious. Although there are studies that attempt to use active learning-based methods to assist in the annotation process, these methods still require some amount of effort from the user and are impractical, especially when implementing a home for the elderly. In this paper, we propose an automated labelling approach using a transformer-based deep learning model to label the daily activities. Our method leverages on spatio-temporal information for class annotation. We evaluated our method on publicly available datasets.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Automated data labelling, deep learning, transformers, activity recognition, smart homes
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Dec 2024 00:57
Last Modified:	03 Dec 2024 00:57
URII:	http://shdl.mmu.edu.my/id/eprint/13150

Downloads

Edit (login required)