detecting gait phases from rgb-d images base on hidden markov model

gait contains important information about the status of the human body and physiological signs. in many medical applications, it isimportant 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 ofhuman gait from rgb-depth images is presented. the sequence of depth images from the front view has been processed to extractthe lower body depth profile and distance features. feature vector extracted from image is the same as observation vector of hiddenmarkov model, and the phases of gait are considered as hidden states of the model. after training the model using the images whichare randomly selected as training samples, the phase estimation of gait becomes possible using the model. the results confirm therate of 60–40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision.