A machine learning approach to fall detection algorithm using wearable sensor

Chia Yeh Hsieh, Chih Ning Huang, Kai Chun Liu, Woei Chyn Chu, Chia Tai Chan

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

27 Scopus citations

Abstract

Falls are the primary cause of accidents for the elderly in living environment. Falls frequently cause fatal and non-fatal injuries that are associated with a large amount of medical costs. Reduction hazards in living environment and doing exercise for training balance and muscle are the common strategies for fall prevention. But falls cannot be avoided completely; fall detection provides the alarm in time that can decrease the injuries or death caused by no rescue. We propose machine learning-based fall detection algorithm using multi-SVM with linear, quadratic or polynomial kernel function, and k-NN classifier. Eight kinds of falling postures and seven types of daily activities arranged in the experiment are used to explore the performance of the machine learning-based fall detection algorithm. The emulated falls were performed on a soft mat by ten healthy young subjects wearing protectors. The k-nearest neighbor method with 0.1 second window size has the highest accuracy, which is 96.26%. The results show that the proposed machine learning fall detection algorithm can fulfill the requirements of adaptability and flexibility for the individual differences.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Advanced Materials for Science and Engineering
Subtitle of host publicationInnovation, Science and Engineering, IEEE-ICAMSE 2016
EditorsTeen-Hang Meen, Stephen D. Prior, Artde Donald Kin-Tak Lam
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages707-710
Number of pages4
ISBN (Electronic)9781509038695
DOIs
StatePublished - 2 Feb 2017
Event2016 IEEE International Conference on Advanced Materials for Science and Engineering, IEEE-ICAMSE 2016 - Tainan, Taiwan
Duration: 12 Nov 201613 Nov 2016

Publication series

NameProceedings of the IEEE International Conference on Advanced Materials for Science and Engineering: Innovation, Science and Engineering, IEEE-ICAMSE 2016

Conference

Conference2016 IEEE International Conference on Advanced Materials for Science and Engineering, IEEE-ICAMSE 2016
Country/TerritoryTaiwan
CityTainan
Period12/11/1613/11/16

Keywords

  • fall detection
  • machine learning
  • wearable sensor

Fingerprint

Dive into the research topics of 'A machine learning approach to fall detection algorithm using wearable sensor'. Together they form a unique fingerprint.

Cite this