Detection of Abnormal Gait From Skeleton Data

Meng Meng (1) , Hassen Drira (2) , Mohamed Daoudi (2) , Jacques Boonaert (3)

1 TUST - Tianjin University of Science and Technology
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 CERI SN - IMT Nord Europe - Centre for Digital Systems

Human gait analysis has becomes of special interest to computer vision community in recent years. The recently developed commodity depth sensors bring new opportunities in this domain.In this paper, we study the human gait using non intrusive sensors (Kinect 2) in order to classify normal human gait and abnormal ones. We propose the evolution of inter-joints distances as spatio temporal intrinsic feature that have the advantage to be robust to location. We achieve 98% success to classify normal and abnormal gaits and show some relevant features that are able to distinguish them.

Mots clés: Joint distances; Abnormal Gait; Spatio Temporal Modeling

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Meng Meng, Hassen Drira, Mohamed Daoudi, Jacques Boonaert. Detection of Abnormal Gait From Skeleton Data. VISIGRAPP, 2016, Rome, Italy. ⟨hal-01703237⟩