title: Motion analysis of an articulated locomotion model by video and telemetric data.
abstract: Traditional techniques of human motion analysis use markers located on body articulations. The position of each marker is extracted from each image. Temporal and kinematic analysis is given by matching these data with a reference model of the human body. However, as human skin is not rigidly linked with the skeleton, each movement causes displacements of the markers and induces uncertainty in results. Moreover, the experiments are mostly conducted in restricted laboratory conditions. The aim of our project was to develop a new method for human motion analysis which needs non-sophisticated recording devices, avoids constraints to the subject studied, and can be used in various surroundings such as stadiums or gymnasiums. Our approach consisted of identifying and locating body parts in image, without markers, by using a multi-sensory sensor. This sensor exploits both data given by a video camera delivering intensity images, and data given by a 3D sensor delivering in-depth images. Our goal, in this design, was to show up the feasibility of our approach. In any case the hardware we used could facilitate an automated motion analysis. We used a linked segment model which referred to Winter's model, and we applied our method not on a human subject but on a life size articulated locomotion model. Our approach consists of finding the posture of this articulated locomotion model in the image. By performing a telemetric image segmentation, we obtained an approximate correspondence between linked segment model position and locomotion model position. This posture was then improved by injecting segmentation results in an intensity image segmentation algorithm. Several tests were conducted with video/telemetric images taken in an outdoor surrounding with the articulated model. This real life-size model was equipped with movable joints which, in static positions, described two strides of a runner. With our fusion method, we obtained relevant limbs identification and location for most postures.
authors.affiliation: LASMEA, UMR 6602 du CNRS, Université Blaise Pascal de Clermont-Ferrand, Aubiere, France.
authors.forename: P C
authors.initials: PC
authors.lastname: Lugné
authors.forename: J
authors.initials: J
authors.lastname: Alizon
authors.forename: F
authors.initials: F
authors.lastname: Collange
authors.forename: E
authors.initials: E
authors.lastname: Van Praagh
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003199:Computer Systems; D004777:Environment; D004867:Equipment Design; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007596:Joints / Q000502:physiology; D008124:Locomotion / Q000502:physiology*; D008953:Models, Anatomic; D011187:Posture / Q000502:physiology; D012867:Skin; D013686:Telemetry / Q000295:instrumentation; D014743:Videotape Recording / Q000295:instrumentation