Citation

Jaiteh, Sedia and Lee, Lini and Tan, Ching Seong (2022) Classification of human gait based on fine Gaussian support vector machines using a force platform. In: th Innovation and Analytics Conference and Exhibition, IACE 2021, 19 August 2022, Virtual - Kedah, Malaysia.

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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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Gait recognition, Human recognition
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Oct 2022 03:54
Last Modified:	06 Oct 2022 03:54
URII:	http://shdl.mmu.edu.my/id/eprint/10472

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