Open AccessTime series activity classification using gated recurrent units
Author(s) -
Yi Fei Tan,
Xiaoning Guo,
Soon Chang Poh
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i4.pp3551-3558
Subject(s) - sitting , recurrent neural network , computer science , series (stratigraphy) , span (engineering) , artificial intelligence , focus (optics) , pattern recognition (psychology) , artificial neural network , medicine , engineering , structural engineering , biology , pathology , paleontology , physics , optics
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%.