Context Analysis and Estimation of Mobile Users Considering the Time Series of Data

Hiromi Shimizu, Mutsumi Suganuma, Wataru Kameyama

研究成果: Conference contribution

抄録

Recently, the demand for understanding mobile user's activities has been increasing in the fields such as customer behavior analysis. Our previous study shows that mobile user's context can be estimated to some extent using various sensor data of mobile phone and user's bio-signals by applying machine learning methods. In this paper, we propose to analyze and estimate the context by considering the time series of data to improve the accuracy. For the analysis and the estimation, using the data collected from two subjects, convolutional neural network (CNN) and various machine learning methods to classify the data into pre-defined eight and seven contexts are applied and compared. The results show that CNN with 256 window width achieve the highest macro F1-score of 97.6% and 97.1% for each subject, respectively. It suggests that the context analysis and estimation using sensor data and bio-signal can be done much more accurately by considering the time series of data.

本文言語English
ホスト出版物のタイトル2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ379-381
ページ数3
ISBN(電子版)9781728198026
DOI
出版ステータスPublished - 2020 10 13
イベント9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
継続期間: 2020 10 132020 10 16

出版物シリーズ

名前2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
国/地域Japan
CityKobe
Period20/10/1320/10/16

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学
  • メディア記述
  • 器械工学
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識

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