VA algorithm for elderly's falling detection with 2d-pose-estimation

Pichayakul Jenpoomjai, Potsawat Wosri, Somchoke Ruengittinun, Chih Lin Hu, Chalothon Chootong

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

4 Scopus citations

Abstract

This paper aims to reduce the losses in emergency cases of elderly falling in residential living environments. We design a falling detection system that can determine the human pose-estimation using the TensorFlow APIs to identify the falling of seniors. The proposed specific VA algorithm that considers time, velocity and acceleration factors of human movement, the falling detection system can better analyze the falling and obtain more accurate pose-estimation. To examine the proposed system, the experiments were conducted to testify basic specifications of fallings upon real data traces of human motion records. Results show the acceleration of human movement can relatively affect the classification of actions. the proposed approach achieves an accuracy of 88% on the test data on falling detection.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages236-240
Number of pages5
ISBN (Electronic)9781728128207
DOIs
StatePublished - Aug 2019
Event12th International Conference on Ubi-Media Computing, Ubi-Media 2019 - Bali, Indonesia
Duration: 6 Aug 20199 Aug 2019

Publication series

NameProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019

Conference

Conference12th International Conference on Ubi-Media Computing, Ubi-Media 2019
Country/TerritoryIndonesia
CityBali
Period6/08/199/08/19

Keywords

  • Fall detection
  • Health care
  • Pose-estimation

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