Citation

Tan, Yi Fei and Poh, Soon Chang and Ooi, Chee Pun and Tan, Wooi Haw (2023) Human activity recognition with self-attention. International Journal of Electrical and Computer Engineering (IJECE), 13 (2). p. 2023. ISSN 2088-8708

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Abstract

In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.

Item Type:	Article
Uncontrolled Keywords:	Convolution neural network, Human activity recognition, Long short-term memory, Self-attention
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:	31 Jan 2023 06:32
Last Modified:	31 Jan 2023 06:32
URII:	http://shdl.mmu.edu.my/id/eprint/11106

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