Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Strasse 2b, D-91052 Erlangen, Germany.
Department of Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, D-91054 Erlangen, Germany.
Sensors (Basel). 2019 Jul 13;19(14):3103. doi: 10.3390/s19143103.
Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson's disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians' gait assessment and to monitor patients in their daily environment.
使用可穿戴传感器的移动步态分析系统具有以前所未有的精细程度分析和监测病理性步态的潜力。仔细观察帕金森病 (PD) 的步态会发现，转弯有其自身的特点，需要进行专门的分析。本文的目的是介绍一种带有鞋上可穿戴传感器的系统，以便在 PD 的标准化步态测试中分析转弯异常。我们调查了 108 名 PD 患者和 42 名年龄匹配的对照者的转弯异常。我们通过几个时空参数来量化转弯。对转弯相关参数的分析揭示了与不同疾病阶段和运动障碍相关的转弯相关步态障碍的差异。我们的研究结果证实并扩展了之前研究的结果，并展示了我们系统在转弯分析中的适用性。我们的系统可以深入了解 PD 患者的转弯情况，并可作为医生步态评估的补充，并在日常生活环境中监测患者。
