Video analysis of gait for diagnosing movement disorders

Abstract

This paper describes a video analysis system, free of markers and setup procedures, that quantitatively identified gait abnormalities in real time from standard video images. A novel color three-dimensional body model was sized and texture mapped to the exact characteristics of a person from video images. The kinematics of the body model was represented by a transformation tree to track the position and orientation of a person relative to the camera. Joint angles were used to track the location and orientation of each body part, with the range of joint angles being constrained by associating degrees of freedom with each joint. To stabilize tracking, the joint angles were estimated for the next frame. The calculation of joint angles, for the next frame, was cast as an estimation problem, which was solved using an iterated extended Kalman filter. Patients with dopa-responsive Parkinsonism, and age-matched normals, were video taped during several gait cycles with walking movements successfully tracked and classified. The results suggested that this approach has the potential to guide clinicians on the relative sensitivity of specific postural/gait features in diagnosis.

Motion Cue Analysis for Parkinsonian Gait Recognition

“…The previous Linear Discriminant Analysis [6] and correlation [8] methods though produced 95% and 94% recognition rates for PG on other data sets but they are computationally complicated and expensive. These assessment methods required markers to be placed on the subject’s body.…”

“…These assessment methods required markers to be placed on the subject’s body. The approaches [6-8] using the Principal Component Analysis and General Regression Neural Network required extensive laboratory setups and prerequisites for the experiments thus making the methods unfeasible to be used for assessment in the home environment. The marker-free gait assessment method based on cosine similarity described in this paper is the least computationally expensive due to distance computations.…”

“…The foreground pixels of the human body are segmented from the background pixels based on the pixel brightness in each frame. In this way, the need for novel colored dress for laboratory experiment as used by RD Green and Howard Lee [7, 8] is eliminated. The image enhancements are done to extract the 2D silhouette from the gait video frames.…”

“…R.D. Green et al [8] extended the research for PG recognition. The environment settings as used by Howard Lee et al [7] were used to identify gait abnormalities in video images.…”

Position and locality constrained soft coding for human action recognition

“…[4][5][6] However, it still remains a challenging problem because of some factors such as camera motion, cluttered background, occlusion, and varied object appearance. Building a discriminative representation for actions in videos is still challenging for better recognition.…”