Application of КРСА with different kernels for human gait assessment

dc.contributor.author	Marcin Derlatka Ph. D.
dc.date.accessioned	2023-01-20T11:51:58Z
dc.date.available	2023-01-20T11:51:58Z
dc.date.issued	2005
dc.identifier.citation	Marcin Derlatka Ph. D. Application of КРСА with different kernels for human gait assessment / Marcin Derlatka Ph. D. // Журнал Гродненского государственного медицинского университета. – 2005. – № 4 (12). – C. 52-54.	ru_RU
dc.identifier.issn	2221-8785
dc.identifier.uri	http://elib.grsmu.by/handle/files/29130
dc.description	multivariate statistics, human gait, kernel principal component analysis	ru_RU
dc.description.abstract	The evaluation of Kernel Principal Component Analysis (KPCA) based on different kernels in human gain assessment has been made. Three types of kernel have been chosen: linear, polynomial and RBF. The normalcy index was computed to evaluate usefulness kernels in human gait assessing. The test group consists of 45 persons (156 strides) with normal or pathological (Cerebral Palsy, Spina Biftda, Anterior Cruciate Ligament and Gonarthrosis) gait.	ru_RU
dc.language.iso	en	ru_RU
dc.publisher	ГрГМУ	ru_RU
dc.title	Application of КРСА with different kernels for human gait assessment	ru_RU
dc.type	Article	ru_RU