An automated form of video image analysis applied to classification of movement disorders

Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videotaped while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practise. We have begun development of intelligent software which aims to provide a more flexible system able to quantify human posture and movement directly from whole-body images without markers and in an unstructured environment. The steps involved are to extract complete human profiles from video frames, to fit skeletal frameworks to the profiles and derive joint angles and swing distances. By this means a given posture is reduced to a set of basic parameters that can provide input to a neural network classifier. To test the system's performance we videotaped patients with dopa-responsive Parkinsonism and age-matched normals during several gait cycles, to yield 61 patient and 49 normal postures. These postures were reduced to their basic parameters and fed to the neural network classifier in various combinations. The optimal parameter sets (consisting of both swing distances and joint angles) yielded successful classification of normals and patients with an accuracy above 90%. This result demonstrated the feasibility of the approach. The technique has the potential to guide clinicians on the relative sensitivity of specific postural/gait features in diagnosis. Future studies will aim to improve the robustness of the system in providing accurate parameter estimates from subjects wearing a range of clothing, and to further improve discrimination by incorporating more stages of the gait cycle into the analysis.