Nguyen Trong-Nguyen, Huynh Huu-Hung, Meunier Jean
DIRO, University of Montreal, Montreal, QC H3T 1J4, Canada.
The University of Danang - University of Science and Technology, Danang 556361, Vietnam.
Sensors (Basel). 2016 Oct 26;16(11):1792. doi: 10.3390/s16111792.
Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.
人体步态分析在肌肉骨骼疾病诊断中起着重要作用。如果没有关于某种步态模式的先验知识，检测人类行走中的异常情况，如拖着脚走路、腿部僵硬或步态不稳，可能会很困难。我们提出了一种基于正常步态模型检测异常人体步态的方法。我们的模型不是采用彩色图像、轮廓或时空体积来创建，而是基于时间序列中的人体关节位置（骨骼）创建的。我们将每个正常步态图像序列分解为步态周期。每个人体即时姿势由一个特征向量表示，该向量描述了位于下半身的成对骨关节之间的关系。然后使用聚类技术将这些向量转换为码字。基于对应于不同步态周期的多个码字序列创建正常人体步态模型。在检测阶段，将具有低于阈值的正常可能性的步态周期（该阈值在训练步骤中自动确定）视为异常。基于标记的运动捕捉数据和Kinect骨骼的实验结果表明，我们的方法在区分正常和异常步态方面非常有前景，总体准确率为90.12%。
