Industrial Electronics Department, University of Minho, Guimarães, Portugal.
Production and Systems Department, University of Minho, Guimarães, Portugal.
Comput Methods Programs Biomed. 2014 Mar;113(3):736-48. doi: 10.1016/j.cmpb.2013.12.005. Epub 2013 Dec 25.
Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach.
助行器常常被错误地开给患者，导致不满情绪增加，并出现了一些问题，如不适和疼痛。因此，有必要客观评估助行步态对助行器使用者步态模式的影响，与非助行步态进行比较。为此将进行一项步态分析，重点关注时空和运动学参数。然而，步态分析会产生冗余信息，这些信息通常难以解释。本研究旨在解决选择最相关的步态特征以区分助行和非助行步态的问题。为此，提出了一种结合基于遗传算法的进化技术和支持向量机算法的有效方法，以区分带前臂支撑的助行器的助行和非助行步态之间的差异。为了比较目的，验证了其他分类算法。对健康受试者的结果表明，主要差异的特征在于平衡和矢状面关节的运动。这些结果得到临床证据的证实，可得出结论，该技术是一种有效的特征选择方法。
