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

Hiromi Shimizu, Mutsumi Suganuma, Wataru Kameyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages379-381
Number of pages3
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

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

Keywords

  • Bio-signals
  • CNN
  • Context Analysis
  • Context Estimation
  • Machine Learning
  • Mobile User
  • Sensor Data

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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