Bidirectional Gated Recurrent Units For Human Activity Recognition Using Accelerometer Data

被引：14

作者：

Alsarhan, Tamam ^{[1
]}

Alawneh, Luay ^{[1
]}

Al-Zinati, Mohammad ^{[1
]}

Al-Ayyoub, Mahmoud ^{[1
]}

机构：

[1] Jordan Univ Sci & Technol, Irbid, Jordan

来源：

2019 IEEE SENSORS | 2019年

关键词：

Mobile Sensors; Recurrent Neural Networks (RNN); Long-Short Term Memory (LSTM); Classification; NEURAL-NETWORKS;

D O I：

10.1109/sensors43011.2019.8956560

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Human activity recognition aims to detect the type of human movement based on sensor data gathered during human activity. Time series classification using deep learning approaches offers opportunities to avoid intensive handcrafted feature extraction techniques where the efficiency and the accuracy are heavily dependent on the quality of variables defined by domain experts. In this paper, we apply recurrent neural networks on data collected from mobile phone accelerometers for the recognition of human activity. More specifically, we use the bidirectional gated recurrent units mechanism. The results show that this technique is promising and provides high quality recognition results.

引用

页数：4

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