Sliding Window Input on Long Short-Term Memory Networks for Bed Position Classification

Authors

  • Sakada Sao 1School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
  • Virach Sornlertlamvanich School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand, Asia AI Institute, Department of Data Science, Faculty of Data Science, Musashino University, Tokyo 135-8181, Japan

Keywords:

Bed position classification, Elderly care, LSTM, Piezoelectric sensor, Pressure sensor

Abstract

The research paper specifies an approach for bed position classification by using twolayer of the Long Short Term Memory approach. The two types of sensor data from pressure and piezoelectric sensors are collected and classified into 5 classes, namely out of bed, sitting, sleep center, sleep left, and sleep right. In the preprocessing process, the sensor data are normalized by min-max scaling normalization within a set range of 0 to 1 to avoid the scale bias in the training process. The 30Hz sensor sampling rate of data is accumulated to fit a one-second interval. The model has been experimentally evaluated by preprocessing the dataset and varying the number of hidden nodes of the model in 128, 80, and 50 nodes. As a result, 94.74% of the accuracy has been improved comparing to the prior result.

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Published

2022-09-28

How to Cite

Sao, S., & Sornlertlamvanich, V. (2022). Sliding Window Input on Long Short-Term Memory Networks for Bed Position Classification. Science & Technology Asia, 27(3), 137–151. Retrieved from https://ph02.tci-thaijo.org/index.php/SciTechAsia/article/view/239872

Issue

Section

Engineering