Physical activity recognizer based on multimodal sensors in smartphone for ubiquitous-lifecare services

Muhammad Fahim, Asad Masood Khattak, Saiqa Aleem, Haseena Al Katheeri

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

1 Citation (Scopus)

Abstract

Smartphone-based activity recognition is an emerging field of research that enables a large number of human-centric applications in the u-lifecare domain. Currently, major challenges include the development of real-time position independent and lightweight classifier models to recognize the physical activities inside the smartphone environment. In this paper, we propose a real-time position independent physical activity recognizer that utilizes the embedded accelerometer, ambient light and proximity sensors of smartphone to recognize the physical activities. To validate our model, we implement it in an open source Android platform to recognize six physical activities and performed extensive experiments over 10 subjects. We obtained 88% of class-accuracy and 91.55% F-measures. It is expected that our model would be a practical and realistic solution for physical activity recognition due to its unobtrusive nature and real-time classification of activities.

Original languageEnglish
Title of host publication2017 IEEE AFRICON
Subtitle of host publicationScience, Technology and Innovation for Africa, AFRICON 2017
EditorsDarryn R. Cornish
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages524-529
Number of pages6
ISBN (Electronic)9781538627754
DOIs
Publication statusPublished - 07 Nov 2017
Externally publishedYes
EventIEEE AFRICON 2017 - Cape Town, South Africa
Duration: 18 Sept 201720 Sept 2017

Publication series

NameIEEE AFRICON: Proceedings
PublisherIEEE
ISSN (Electronic)2153-0033

Conference

ConferenceIEEE AFRICON 2017
Country/TerritorySouth Africa
CityCape Town
Period18/09/201720/09/2017

Bibliographical note

Funding Information:
This research work was supported by the ADEC Award for Research Excellence (A2RE) 2015. This research was also supported by Zayed University RIF funding # R17063.

Publisher Copyright:
© 2017 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Activity Recognition
  • Smartphone
  • U-lifecare

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications

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