Comparison of deep learing algorithms for indoor monitoring using bioelectric potential of living plants

Hidetaka Nambo, Imam Tahyudin, Takeo Nakano, Tetsuya Yamada

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

This study aims to develop a monitoring system for an indoor space. We are investigating to use the bioelectric potential of living plants as a human sensor system in an indoor environment. The system utilizes a change of the bioelectric potential to estimate a resident's location in a room. To build an estimation model, a lot of the bioelectric potential data are collected and processed by a machine learning method. We have studied to build the estimation model using a convolutional neural network. However, recently, there are many applications that utilize Long-Short Term Memory method for a time sequential data, and they obtained a good result successfully. Therefore, in this study we applied LSTM for the bioelectric potential data and investigate the availability of CNN and LSTM to estimate the location with the bioelectric potential. As the result of classification experiments with the model trained with collected bioelectric data, we obtained that CNN is better than LSTM for this problem. However, we need to improve the accuracy by adjusting parameters in future.

本文言語英語
ホスト出版物のタイトルProceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ110-113
ページ数4
ISBN(電子版)9781538670828
DOI
出版ステータス出版済み - 7 2 2018
外部発表はい
イベント3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018 - Yogyakarta, インドネシア
継続期間: 11 13 201811 14 2018

出版物シリーズ

名前Proceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018

会議

会議3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018
国/地域インドネシア
CityYogyakarta
Period11/13/1811/14/18

All Science Journal Classification (ASJC) codes

  • 情報システムおよび情報管理
  • 情報システム
  • 電子工学および電気工学
  • 健康情報学
  • 器械工学
  • 人工知能
  • コンピュータ サイエンスの応用

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