title: Automatic gesture analysis using constant affine velocity.
abstract: Hand human gesture recognition has been an important research topic widely studied around the world, as this field offers the ability to identify, recognize, and analyze human gestures in order to control devices or to interact with computer interfaces. In particular, in medical training, this approach is an important tool that can be used to obtain an objective evaluation of a procedure performance. In this paper, some obstetrical gestures, acquired by a forceps, were studied with the hypothesis that, as the scribbling and drawing movements, they obey the one-sixth power law, an empirical relationship which connects path curvature, torsion, and euclidean velocity. Our results show that obstetrical gestures have a constant affine velocity, which is different for each type of gesture and based on this idea this quantity is proposed as an appropriate classification feature in the hand human gesture recognition field.
authors.forename: Jenny
authors.initials: J
authors.lastname: Cifuentes
authors.forename: Pierre
authors.initials: P
authors.lastname: Boulanger
authors.forename: Minh Tu
authors.initials: MT
authors.lastname: Pham
authors.forename: Richard
authors.initials: R
authors.lastname: Moreau
authors.forename: Flavio
authors.initials: F
authors.lastname: Prieto
mesh_terms: D000465:Algorithms; D000886:Anthropometry; D003198:Computer Simulation; D005260:Female; D005868:Gestures; D006225:Hand / Q000502:physiology*; D006257:Head / Q000196:embryology / Q000502:physiology; D006801:Humans; D007743:Labor, Obstetric; D016014:Linear Models; D008328:Man-Machine Systems; D009068:Movement; D009772:Obstetrical Forceps; D009774:Obstetrics / Q000379:methods*; D010363:Pattern Recognition, Automated; D010388:Pelvis / Q000502:physiology; D011247:Pregnancy; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface