Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke
Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke
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DOI：
10.1016/s0966-6362(02)00165-0
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发表时间：
2003-08-01
期刊：
影响因子：
2.4
通讯作者：
Perry, J
中科院分区：
文献类型：
--
作者：
Mulroy, S;Gronley, J;Perry, J
中风后患者的步态偏差的混合是非常不同的。该人群步态模式分类的客观系统可以用来指导治疗计划。47名患者入院接受康复治疗，42名患者在卒中后6个月再次进行步态定量分析。根据步行的时间、空间和运动学参数，采用非系统聚类法对患者的步态模式进行分类。在两个评估间隔中确定了四组患者。在入院测试中，步行速度、中立位最大伸膝和挥杆最大背屈是最能表征这三组人的三个因素。在6个月时，解释变量是速度、末端站立时的膝关节伸展和摆动前的膝关节屈曲。在步行过程中，肌肉力量和肌肉激活模式在不同组之间存在差异。(C)2002 Elsevier Science B.V.保留所有权利。
The mixture of gait deviations seen in patients following a stroke is remarkably variable. An objective system for classification of gait patterns for this population could be used to guide treatment planning. Quantitated gait analysis was conducted for 47 individuals at admission to in-patient rehabilitation and again at 6 months post-stroke for 42 subjects. Non-hierarchical cluster analysis was used to classify the gait patterns of patients based on the temporal-spatial and kinematic parameters of walking. Four clusters of patients were identified at both assessment intervals. At the admission test walking velocity, peak knee extension in mid stance and peak dorsiflexion in swing were the three factors that best characterized the groups. At 6 months the explanatory variables were velocity, knee extension in terminal stance, and knee flexion in pre swing. Differences in muscle strength and muscle activation patterns during walking were identified between groups. (C) 2002 Elsevier Science B.V. All rights reserved.