Context analysis and estimation of mobile users by using bio-signals and sensor data

Hiromi Shimizu, Mutsumi Suganuma, Wataru Kameyama

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

Abstract

The sensor data obtained from mobile and wearable devices are useful to analyze and estimate user's context, but also user's bio-signals are, because they may reflect user's psychological aspects in the corresponding context. Therefore, in this paper, we focus on context analysis and estimation of mobile users by using bio-signals and sensor data of mobile devices. For the analysis and estimation, various machine learning methods are applied to classify the data into pre-defined six contexts. The evaluation shows that Gradient Boosting Decision Tree achieves the highest classification accuracy of about 80% in supervised methods, and Sparse Representation-based Classification achieves more than 90% accuracy. The results suggest that the context analysis and estimation can be done accurately by using bio-signals and sensor data.

Original languageEnglish
Title of host publication2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-266
Number of pages4
ISBN (Electronic)9781728135755
DOIs
Publication statusPublished - 2019 Oct
Event8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, Japan
Duration: 2019 Oct 152019 Oct 18

Publication series

Name2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019

Conference

Conference8th IEEE Global Conference on Consumer Electronics, GCCE 2019
CountryJapan
CityOsaka
Period19/10/1519/10/18

Keywords

  • Bio-signals
  • Context analysis
  • Context estimation
  • Machine learning
  • Sensor data

ASJC Scopus subject areas

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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