Long Short-Term Memory for Bed Position Classification
Sakada Sao, Virach Sornlertlamvanich · 2019
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.