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

Jaiteh, Sedia; Lee, Lini; Tan, Ching Seong

doi:10.1063/5.0092635

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…Source [ 62 ] proposed a special version of a convolutional-recurrent neural network (KineticNet) which, based on vertical, anterior/posterior of GRFs as well as one of the coordinates of the center of pressure, was able to re-identify 118 subjects with approximately 96% accuracy. Similar results were obtained by [ 63 ] where an accuracy of 94% was observed using Fine Gaussian Support Vector Machines and GRFs. The authors, unusually, determined training time to be 3.312 s but the research concerned only 5 people.…”

Section: Discussionsupporting

confidence: 89%

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.

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Section: Discussionsupporting

confidence: 89%