Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia.
N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia.
Sensors (Basel). 2022 Sep 21;22(19):7178. doi: 10.3390/s22197178.
The quality of modern measuring instruments has a strong influence on the speed of diagnosing diseases of the human musculoskeletal system. The research is focused on automatization of the method of gait analysis. The study involved six healthy subjects. The subjects walk straight. Each subject made several gait types: casual walking and imitation of a non-standard gait, including shuffling, lameness, clubfoot, walking from the heel, rolling from heel to toe, walking with hands in pockets, and catwalk. Each type of gait was recorded three times. For video fixation, the Vicon Nexus system was used. A total of 27 reflective markers were placed on the special anatomical regions. The goniometry methods were used. The walk data were divided by steps and by step phases. Kinematic parameters for estimation were formulated and calculated. An approach for data clusterization is presented. For this purpose, angle data were interpolated and the interpolation coefficients were used for clustering the data. The data were processed and four cluster groups were found. Typical angulograms for cluster groups were presented. For each group, average angles were calculated. A statistically significant difference was found between received cluster groups.
现代测量仪器的质量对诊断人类运动系统疾病的速度有很大影响。本研究专注于步态分析方法的自动化。研究涉及 6 名健康受试者。受试者直线行走。每位受试者进行了几种步态类型：随意行走和非标准步态模仿，包括拖曳步态、跛行、马蹄足、脚跟行走、从脚跟到脚趾滚动、手插口袋行走和猫步。每种步态类型记录 3 次。使用 Vicon Nexus 系统进行视频固定。在特殊解剖区域放置了总共 27 个反光标记。使用关节角度测量法。将行走数据按步和步相进行划分。制定并计算了运动学参数。提出了一种数据聚类方法。为此，对角度数据进行了插值，并且使用插值系数对数据进行聚类。对数据进行了处理，发现了四个聚类组。呈现了聚类组的典型角度图。为每个组计算了平均角度。在接收到的聚类组之间发现了统计学上的显著差异。
