Classification of human gait based on fine Gaussian support vector machines using a force platform

Sedia Jaiteh, Lini Lee, Ching Seong Tan

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

1 Citation (Scopus)

Abstract

A force platform prototype for human gait classification as an alternative to vision-based and wearable sensor-based gait classification technologies is proposed. The primary sensors involved in this prototype were load cells. When a volunteer walks on the force platform, the load cells record signal changes corresponding to the walking pattern of the volunteer. These signals were digitized and amplified and stored in a micro-SD card. Five gait features were extracted from the stored data in the micro-SD card, and MATLAB classification learner was used for classification. An accuracy of 94% was observed with Fine Gaussian Support Vector Machines. This shows that the force platform is a good alternative to vision-based and wearable sensor-based gait classification technologies.

Original languageEnglish
Title of host publication5th Innovation and Analytics Conference and Exhibition, IACE 2021
EditorsHaslinda Ibrahim, Jafri Zulkepli, Nazrina Aziz, Ma. Carlota Blajadia Decena, Abdul Malek Bin Yaakob, Sahubar Khan, Josephine Bernadette M. Benjamin
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735443877
DOIs
Publication statusPublished - 19 Aug 2022
Externally publishedYes
Event5th Innovation and Analytics Conference and Exhibition, IACE 2021 - Kedah, Virtual, Malaysia
Duration: 23 Nov 202124 Nov 2021

Publication series

NameAIP Conference Proceedings
Volume2472
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th Innovation and Analytics Conference and Exhibition, IACE 2021
Country/TerritoryMalaysia
CityKedah, Virtual
Period23/11/2124/11/21

ASJC Scopus subject areas

  • General Physics and Astronomy

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