Department of Information Engineering, University of Padova, Padova, Italy; Department of Medicine, DIMED, University of Padova, Padova, Italy.
Ibirapuera University, São Paulo, Brazil; Physical Therapy, Speech and Occupational Therapy Department, School of Medicine, University of Sᾶo Paulo, São Paulo, Brazil.
Gait Posture. 2020 Jun;79:33-40. doi: 10.1016/j.gaitpost.2020.03.016. Epub 2020 Apr 17.
It is well recognized that diabetes and peripheral neuropathy have a detrimental effect on gait. However, there are large variations in the results of studies addressing this aspect due to the heterogeneity of diabetic population in relation to presence and severity of diabetes complications. The aim of this study is to adopt an unsupervised classification technique to better elucidate the gait changes throughout the entire spectrum of diabetes and neuropathy.
Sixty subjects were assessed and classified into four groups using a fuzzy logic model: 13 controls (55 ± 7years), 18 diabetics subjects without neuropathy (59 ± 6 years, 11 ± 7 diabetes years), 7 with mild neuropathy (56 ± 4years, 19 ± 7 diabetes years), and 22 with moderate to severe neuropathy (57 ± 5 years, 14 ± 8 diabetes years). Data were gathered by six infrared cameras at 100 Hz regarding lower limb joint kinematics (angles and angular velocities) and the relative phase for the hip-ankle, hip-knee, and knee-ankle were calculated. The K-means clustering algorithm was adopted to classify subjects considering the whole kinematics time series. A one-way ANOVA test was used to compare both clinical and kinematics parameters across clusters.
Only the classification based on the intralimb coordination variables succeeded in defining 5 well separated clusters with the following clinical characteristics: controls were grouped mainly in Cluster 2, diabetics in Cluster 4, and neuropathic subjects in Cluster 5 (which included various degrees of severity). Hip-ankle coordination in Clusters 4 and 5 were significantly different (p < 0.05) with respect to Cluster 2, mainly in the stance phase. During the swing phase, differences were observed in the ankle-knee coordination (p < 0.05) across clusters.
Classification based on intralimb coordination patterns succeeded in efficiently categorize gait alterations in diabetic subjects. It can be speculated that variables extracted from sagittal plane kinematics might be adopted as a support to clinical decision making in diabetes.
众所周知，糖尿病和周围神经病变会对步态产生不利影响。然而，由于糖尿病患者群体在糖尿病并发症的存在和严重程度方面存在很大差异，因此研究这一方面的结果存在很大差异。本研究旨在采用无监督分类技术更好地阐明整个糖尿病和神经病变范围内的步态变化。
使用模糊逻辑模型将 60 名受试者分为四组：13 名对照者（55 ± 7 岁），18 名无神经病变的糖尿病患者（59 ± 6 岁，11 ± 7 年糖尿病史），7 名轻度神经病变患者（56 ± 4 岁，19 ± 7 年糖尿病史）和 22 名中重度神经病变患者（57 ± 5 岁，14 ± 8 年糖尿病史）。通过六台以 100 Hz 采集下肢关节运动学（角度和角速度）数据的红外摄像机，计算髋关节-踝关节、髋关节-膝关节和膝关节-踝关节的相对相位。采用 K-均值聚类算法对考虑整个运动学时间序列的受试者进行分类。采用单向方差分析比较各组之间的临床和运动学参数。
只有基于肢体内协调变量的分类才能成功定义 5 个分离良好的集群，具有以下临床特征：对照组主要分为第 2 集群，糖尿病患者分为第 4 集群，神经病变患者分为第 5 集群（包括不同程度的严重程度）。第 4 集群和第 5 集群的髋关节-踝关节协调明显不同于第 2 集群（p < 0.05），主要在站立期。在摆动期，各集群之间观察到踝关节-膝关节协调（p < 0.05）的差异。
基于肢体内协调模式的分类成功地有效地对糖尿病患者的步态改变进行分类。可以推测，从矢状面运动学中提取的变量可以作为糖尿病临床决策的支持。
