Time series activity classification using gated recurrent units

Yi-Fei Tan (Multimedia University)
Xiaoning Guo (Multimedia University)
Soon-Chang Poh (Multimedia University)

International Journal of Electrical and Computer Engineering
Vol 11, No 4: August 2021

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%.