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.

中文翻译：

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支持向量机基于RGB-D相机的步行模式识别，用于智能滚轮。

本文提出了一种智能助行车的行走模式检测方法。该方法从RGB-D相机的深度数据中检测滑行者用户的下肢。然后将下肢的3D点数据分割为腿部和脚部数据点，从中提取具有6个骨骼点和4个杆的骨骼系统，并用来表示步行步态。然后构造包括步态形状和步态运动的参数的步态特征以描述步行状态。使用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。马尔可夫链的静态分布表示步行模式。训练了三个支持向量机（SVM）进行步行模式检测。每个SVM检测三个步行模式之一。实验结果表明，与现有的七种方法相比，该方法具有更好的步行模式检测性能。