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Video methods for gait analysis in daily environment

Gait is one of important features to assess elderly physical condition which is directly relative to health status. The changes or abnormalities in gait may reflect risks in health. Different kinds of gait analysis methods have been proposed, such as pressure sensors based method and wearable equipment based method. Recently, with the development of computer vision technologies, more automatic, effective and non-intrusive ways are demanded to measure gait at either nursing facility or at-home in daily environment. In this paper, we propose an automatic approach for non-cooperative persons for gait analysis using 3D camera. This approach applies a tracking based recognition method to identify the targets on captured videos. Then 3D skeleton based behavior analysis is performed to select skeleton series of walking from daily behaviors. Finally gait characteristics are defined and calculated on selected skeleton series of each identified person for gait ability evaluation. Evaluations have been performed on a real-world environment where people do not stop in front of the camera. The result shows that the accuracy of recognition and behavior analysis method reaches above 90% for multiple persons, which is better efficiency and comparable accuracy than the previous methods and our approach is suitable for gait analysis in daily environment.