Pathology Classification Of Gait Human Gestures

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 Sigma-Delta operator. The strategy was evaluated in three groups of patients: normal, musculoskeletal disorders and parkinsons disease, obtaining a precision and a recall of about 60%