Detecting abnormal gait

Analyzing human gait has become popular in computer vision. So far, however, contributions to this topic almost exclusively considered the problem of person identification. In this paper, we view gait analysis from a different angle and shall examine its use as a means to deduce the physical condition of people. Understanding the detection of unusual movement patterns as a two class problem leads to the idea of using support vector machines for classification. We thus present a homeomorphisms between 2D lattices and binary shapes that provides a robust vector space embedding of body silhouettes. Experimental results underline that feature vectors obtained from this scheme are well suited to detect abnormal gait wavering, faltering, and falling can be detected reliably across individuals without tracking or recognizing limbs or body parts.