Classification of Stroke and Non-Stroke Patients from Human Body Movements using Smartphone Videos and Deep Neural Networks

Zafira Binta Feliandra, Siti Khadijah, Muhammad Febrian Rachmadi, Dina Chahyati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study covers a pilot study on developing a tele-health system for detection and classification of stroke and non-stroke patients from human body movements using smartphone videos. Human body poses are extracted from smartphone videos which are then transformed into RGB images and classified into either stroke (positive) or non-stroke (negative) labels. We tested PoseNet, BlazePose, and MoveNet for human body pose detection and AlexN et and SqueezeN et for classification. From this pilot study, we found that MoveNet is the best human body pose detection while AlexNet is the best for classification.

Original languageEnglish
Title of host publicationProceedings - ICACSIS 2022
Subtitle of host publication14th International Conference on Advanced Computer Science and Information Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages187-192
Number of pages6
ISBN (Electronic)9781665489362
DOIs
Publication statusPublished - 2022
Event14th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2022 - Virtual, Online, Indonesia
Duration: 1 Oct 20223 Oct 2022

Publication series

NameProceedings - ICACSIS 2022: 14th International Conference on Advanced Computer Science and Information Systems

Conference

Conference14th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period1/10/223/10/22

Keywords

  • classification
  • human body movements analysis
  • smartphone videos
  • stroke movements detection

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