Long Short-Term Memory for Bed Position Classification

被引：0
|
作者：
Sao, Sakada [1 ]
Sornlertlamvanich, Virach [1 ,2 ]
机构：
[1] Thammasat Univ, Sch Informat Comp & Commun Technol, Sirindhorn Int Inst Technol, Bangkok, Thailand
[2] Musashino Univ, Dept Data Sci, Fac Data Sci, Tokyo, Japan
来源：
PROCEEDINGS OF THE 2019 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY (INCIT): ENCOMPASSING INTELLIGENT TECHNOLOGY AND INNOVATION TOWARDS THE NEW ERA OF HUMAN LIFE | 2019年
关键词：
bed position classification; LSTM; piezoelectric sensor; pressure sensor; elderly care;
D O I：
10.1109/incit.2019.8912080
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper describes an approach for bed position classification by using 2 stacked layers of Long Short-Term Memory approach. The data is collected from the sensor panel which consists of 2 types of sensors, i.e. piezoelectric and pressure sensors. The raw data has been classified into 5 classes. It also has to go through the min-max scaling normalization on a fixed range between 0 and 1. The data is assembled to fit a one-second interval of the 30Hz sensor sampling rate. The model has been experimented by changing the number of hidden nodes of the model in 128, 80 and 50 nodes. The result is 91.70% of accuracy which is good enough comparing to the previous works.
引用
收藏
页码：28 / 31
页数：4
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