@inproceedings{aae721626ba0455bb33576f6c43cd88f,
title = "Classification of human gait based on fine Gaussian support vector machines using a force platform",
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.",
author = "Sedia Jaiteh and Lini Lee and Tan, \{Ching Seong\}",
note = "Publisher Copyright: {\textcopyright} 2022 Author(s).; 5th Innovation and Analytics Conference and Exhibition, IACE 2021 ; Conference date: 23-11-2021 Through 24-11-2021",
year = "2022",
month = aug,
day = "19",
doi = "10.1063/5.0092635",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Haslinda Ibrahim and Jafri Zulkepli and Nazrina Aziz and Decena, \{Ma. Carlota Blajadia\} and \{Bin Yaakob\}, \{Abdul Malek\} and Sahubar Khan and Benjamin, \{Josephine Bernadette M.\}",
booktitle = "5th Innovation and Analytics Conference and Exhibition, IACE 2021",
}