Wang Xingchen, Ristic-Durrant Danijela, Spranger Matthias, Graser Axel
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:467-472. doi: 10.1109/ICORR.2017.8009292.
In this paper, a novel gait assessment system based on measures of gait variability reflected through the variability of shapes of gait cycles trajectories is proposed. The presented gait assessment system is based on SVM (support vector machine) classifier and on gait variability-based features calculated from the hip and knee joint angle trajectories recorded using wearable IMUs during walking trials. A system classifier was trained to distinguish healthy gait patterns from the pathological ones. The features were extracted by calculating the distances between the joint trajectories of the individual gait cycles using 4 different distance functions. As result, the system is able to provide a Gait Variability Index (GVI), which is a numeric value that can be used as an indicator of a degree to which a pathological gait pattern is close to a healthy gait pattern. The system and GVI were tested in three experiments, involving subjects suffering from gait disorders caused by different neurological diseases. The results demonstrated that the proposed gait assessment system would be suitable for supporting clinicians in the evaluation of gait performances during the gait rehabilitation procedures.
本文提出了一种新颖的步态评估系统，该系统基于通过步态周期轨迹形状的变化所反映出的步态变异性测量。所呈现的步态评估系统基于支持向量机（SVM）分类器以及从步行试验期间使用可穿戴惯性测量单元（IMU）记录的髋部和膝部关节角度轨迹计算得出的基于步态变异性的特征。训练了一个系统分类器，以区分健康的步态模式和病理步态模式。通过使用4种不同的距离函数计算各个步态周期的关节轨迹之间的距离来提取特征。结果，该系统能够提供一个步态变异性指数（GVI），这是一个数值，可作为病理步态模式接近健康步态模式程度的指标。该系统和GVI在三个实验中进行了测试，涉及患有由不同神经系统疾病引起的步态障碍的受试者。结果表明，所提出的步态评估系统将适用于在步态康复过程中支持临床医生评估步态表现。
