Time series activity classification using gated recurrent units

Yi-Fei Tan, Xiaoning Guo, Soon-Chang Poh

Abstract


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%.

Keywords


activity classification; AReM dataset; gated recurrent units; recurrent neural network; time series;



DOI: http://doi.org/10.11591/ijece.v11i4.pp%25p
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ISSN 2088-8708, e-ISSN 2722-2578