Zhang He, Ye Cang
Dept. of Systems Engineering, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204.
Int J Intell Robot Appl. 2017 Feb;1(1):32-42. doi: 10.1007/s41315-016-0002-6. Epub 2017 Jan 4.
This paper presents a walking pattern detection method for a smart rollator. The method detects the rollator user's lower extremities from the depth data of an RGB-D camera. It then segments the 3D point data of the lower extremities into the leg and foot data points, from which a skeletal system with 6 skeletal points and 4 rods is extracted and used to represent a walking gait. A gait feature, comprising the parameters of the gait shape and gait motion, is then constructed to describe a walking state. K-means clustering is employed to cluster all gait features obtained from a number of walking videos into 6 key gait features. Using these key gait features, a walking video sequence is modeled as a Markov chain. The stationary distribution of the Markov chain represents the walking pattern. Three Support Vector Machines (SVMs) are trained for walking pattern detection. Each SVM detects one of the three walking patterns. Experimental results demonstrate that the proposed method has a better performance in detecting walking patterns than seven existing methods.
本文提出了一种用于智能助行器的步行模式检测方法。该方法从RGB-D相机的深度数据中检测助行器使用者的下肢。然后将下肢的3D点数据分割为腿部和脚部数据点，从中提取一个具有6个骨骼点和4根杆的骨骼系统，并用其表示步行步态。接着构建一个由步态形状和步态运动参数组成的步态特征来描述步行状态。采用K均值聚类将从多个步行视频中获得的所有步态特征聚类为6个关键步态特征。利用这些关键步态特征，将步行视频序列建模为马尔可夫链。马尔可夫链的平稳分布代表步行模式。训练了三个支持向量机（SVM）用于步行模式检测。每个SVM检测三种步行模式中的一种。实验结果表明，与现有的七种方法相比，该方法在检测步行模式方面具有更好的性能。
