Heravi Hamed, Ebrahimi Afshin, Olyaee Ehsan
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
J Med Signals Sens. 2016 Jul-Sep;6(3):158-65.
Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60-40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision.
步态包含有关人体状态和生理体征的重要信息。在许多医学应用中，监测并准确分析患者的步态非常重要。由于行走在几个阶段呈现出可重复性特征，分离这些阶段可用于步态分析。在本研究中，提出了一种基于图像处理的方法，用于从RGB深度图像中提取人类步态的阶段。对来自正视图的深度图像序列进行处理，以提取下半身深度轮廓和距离特征。从图像中提取的特征向量与隐马尔可夫模型的观测向量相同，并且步态阶段被视为该模型的隐藏状态。在使用随机选择作为训练样本的图像对模型进行训练之后，利用该模型进行步态阶段估计成为可能。结果证实了步态两个主要阶段的比例为60 - 40%，并且中间站立阶段的识别精度为85%。
