During contraction and stretching, muscles change shape and size, and produce a deformation of skin tissues and a modification of the body segment shape. In human motion analysis, it is very important to take into account these phenomena. The aim of this work is the evaluation of skin and muscular deformation, and the modeling of body segment elastic behavior obtained by analysing video sequences that capture a muscle contraction. The soft tissue modeling is accomplished by using triangular meshes that automatically adapt to the body segment during the execution of a static muscle contraction. The adaptive triangular mesh is built on reference points whose motion is estimated by using non linear operators. Experimental results, obtained by applying the proposed method to several video sequences, where biceps brachial isometric contraction was present, show the effectiveness of this technique.
Human movement analysis is generally performed through the utilization of marker-based systems, which allow reconstructing, with high levels of accuracy, the trajectories of markers allocated on specific points of the human body. Marker based systems, however, show some drawbacks that can be overcome by the use of video systems applying markerless techniques. In this paper, a specifically designed computer vision technique for the detection and tracking of relevant body points is presented. It is based on the Gauss-Laguerre Decomposition, and a Principal Component Analysis Technique (PCA) is used to circumscribe the region of interest. Results obtained on both synthetic and experimental tests provide significant reduction of the computational costs, with no significant reduction of the tracking accuracy.
During contraction and stretching, muscles change shape and size, and produce a deformation of skin tissues and a modification of the body segment shape. In human motion analysis, it is indispensable to take into account this phenomenon and thus approximating body limbs to rigid structures appears as restrictive. The present work aims at evaluating skin and muscular deformation, and at modeling body segment elastic behavior by analysing video sequences that capture a sport gesture. The soft tissue modeling is accomplished by using triangular meshes that automatically adapt to the body segment during the execution of a static muscle contraction. The adaptive triangular mesh is built on reference points whose motion is estimated by using the technique based on Gauss Laguerre Expansion. Promising results have been obtained by applying the proposed method to a video sequence, where an upper arm isometric contraction was present.
Postural ability can be evaluated through the analysis of body oscillations, by estimating the displacements of selected sets of body segments. The analysis of human movement is generally achieved through the exploitation of stereophotogrammetric systems that rely on the use of markers. Marker systems show a high cost and patient settings which can be uncomfortable. On the other hand, the use of force platform has some disadvantages: the acquisition of dynamics data permits to estimate only the body oscillations as a whole, without any information about individual body segment movements. Some of these drawbacks can be overcome by the use of video systems, applying a marker-free sub-pixel algorithm. In this paper, a novel method to evaluate balance strategies that utilises commercial available systems and applies methods for feature extraction and image processing algorithms is presented.
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