Authors:
Fabio Martínez
;
Juan Carlos León
and
Eduardo Romero
Affiliation:
National University of Colombia, Colombia
Keyword(s):
Gesture recognition, Human motion analysis, Gait analysis, Markerless approach.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
Abstract:
Gait patterns may be distorted in a large set of pathologies. In the clinical practice, the gait is studied using a set of measurements which allows identification of pathological disorders, thereby facilitating diagnosis, treatment and follow up. These measurements are obtained from a set of markers, carefully placed in some specific anatomical locations. This conventional procedure is obviously invasive and alters the natural movement gestures, a great drawback for diagnosis and management of the early disease stages, when accuracy is a crucial issue. Instead, markerless approaches attempt to capture the very nature of the movement with practically no intervention on the movement patterns. These techniques remain still limited concernig their clinical applications since they do not segment with sufficient precision the human silhouette. This article introduces a novel markerless strategy for classiying normal and pathological gaits, using a temporal-spatial characterization of the
subject from 2 differents views. The feature vector is constructed by associating the spatial information obtained with SURF and the temporal information from a ∑-∆ operator. The strategy was evaluated in three groups of patients: normal, musculoskeletal disorders and parkinson’s disease, obtaining a precision and a recall of about 60%
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