Abstract

BRAVO, RICARDO JOSÉ; DE CASTRO, OSBERTH CRISTHIAN  and  SALAZAR, ANTONIO JOSÉ. Spastic hemiplegia gait characterization using support vector machines: Contralateral lower limb. Rev. Fac. Ing. UCV [online]. 2006, vol.21, n.2, pp.111-119. ISSN 0798-4065.

Spastic Hemiplegia (SH) is a brain motor dysfunction with neuromuscular implications on one side of the body which leads to gait disorders. The gait of such dysfunction has been described and classified in terms of its affected side lower limb (known as ipsilateral limb) measurements using manual analytical methods. It has been assumed that the unaffected side limb (known as contralateral limb) compensates gait deviations due to the abnormal pattern of the ipsilateral limb. But in gait, the behavior of both limbs is highly correlated so analysis of the contralateral side should prove useful, although there are a lack of studies regarding contralateral limbs. This study is part of an ongoing effort to analyze the SH gait pathology in terms of limbs of both sides and it begins with the relationship between ipsilateral SH gait pattern classification versus contralateral limb compensating pattern. In this work, the focus has been on the kinematics of the unaffected contralateral limb of the disorder taking advantage of high profile statistical learning computational methods, such as Support Vector Machines (SVM) models. Results showed that consistent types of SH kinematics patterns can be found, described and also characterized using a SVM model. Further improvements in the accuracy of SH classification and characterization are under way.

Keywords : Gait; Kinematics; Hemiplegia; Learning; Pattern Recognition; Support Vector Machines.