Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 03 Hradec Králové, Czech Republic.
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Praha 6, Czech Republic.
Sensors (Basel). 2021 Aug 19;21(16):5576. doi: 10.3390/s21165576.
Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.
步态障碍伴随着许多神经和肌肉骨骼疾病，这些疾病显著降低了生活质量。运动传感器可以对步态模式进行高质量的建模。然而，它们会产生大量的数据，评估这些数据是一个挑战。在本出版物中，我们比较了不同的数据减少方法和用于临床实践的减少后数据的分类。通过 t 分布随机邻域嵌入预处理的随机森林分类器从 43 名参与者（23 名共济失调，20 名健康）的记录中提取的一组健康个体和共济失调步态患者的记录中，最佳准确率为 98%，共提取了 418 段直走模式的片段。
