Automatic detection of abnormal gait

Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. Understanding the detection of unusual movement patterns as a two-class problem suggests using support vector machines for classification. We present a homeomorphisms between 2D lattices and binary shapes that provides a robust vector space embedding of segmented body silhouettes. Experimental results demonstrate 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 recognising limbs or body parts.