Time series activity classification using gated recurrent units


Tan, Yi Fei and Guo, Xiaoning and Poh, Soon Chang (2021) Time series activity classification using gated recurrent units. International Journal of Electrical and Computer Engineering (IJECE), 11 (4). p. 3551. ISSN 2088-8708

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The population of elderly is growing and is projected to outnumber the youth in the future. Many researches on elderly assisted living technology were carried out. One of the focus areas is activity monitoring of the elderly. AReM dataset is a time series activity recognition dataset for seven different types of activities, which are bending 1, bending 2, cycling, lying, sitting, standing and walking. In the original paper, the author used a many-to-many Recurrent Neural Network for activity recognition. Here, we introduced a time series classification method where Gated Recurrent Units with many-to-one architecture were used for activity classification. The experimental results obtained showed an excellent accuracy of 97.14%.

Item Type: Article
Uncontrolled Keywords: Neural networks (Computer science)
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 Jul 2021 03:25
Last Modified: 01 Jul 2021 03:25
URII: http://shdl.mmu.edu.my/id/eprint/8794


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