Literature DB >> 16783657

A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach.

S Corazza1, L Mündermann, A M Chaudhari, T Demattio, C Cobelli, T P Andriacchi.   

Abstract

Human motion capture is frequently used to study musculoskeletal biomechanics and clinical problems, as well as to provide realistic animation for the entertainment industry. The most popular technique for human motion capture uses markers placed on the skin, despite some important drawbacks including the impediment to the motion by the presence of skin markers and relative movement between the skin where the markers are placed and the underlying bone. The latter makes it difficult to estimate the motion of the underlying bone, which is the variable of interest for biomechanical and clinical applications. A model-based markerless motion capture system is presented in this study, which does not require the placement of any markers on the subject's body. The described method is based on visual hull reconstruction and an a priori model of the subject. A custom version of adapted fast simulated annealing has been developed to match the model to the visual hull. The tracking capability and a quantitative validation of the method were evaluated in a virtual environment for a complete gait cycle. The obtained mean errors, for an entire gait cycle, for knee and hip flexion are respectively 1.5 degrees (+/-3.9 degrees ) and 2.0 degrees (+/-3.0 degrees ), while for knee and hip adduction they are respectively 2.0 degrees (+/-2.3 degrees ) and 1.1 degrees (+/-1.7 degrees ). Results for the ankle and shoulder joints are also presented. Experimental results captured in a gait laboratory with a real subject are also shown to demonstrate the effectiveness and potential of the presented method in a clinical environment.

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Year:  2006        PMID: 16783657     DOI: 10.1007/s10439-006-9122-8

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  29 in total

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7.  Assessing the accuracy of measuring leg length discrepancy and genu varum/valgum using a markerless motion analysis system.

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8.  Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning.

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9.  The test-retest reliability of anatomical co-ordinate axes definition for the quantification of lower extremity kinematics during running.

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10.  The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications.

Authors:  Lars Mündermann; Stefano Corazza; Thomas P Andriacchi
Journal:  J Neuroeng Rehabil       Date:  2006-03-15       Impact factor: 4.262

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