title: Using a marker-less method for estimating L5/S1 moments during symmetrical lifting.
abstract: The aim of this study is to analyze the validity of a computer vision-based method to estimate 3D L5/S1 joint moment during symmetrical lifting. An important criterion to identify the non-ergonomic lifting task is the value of net moment at L5/S1 joint. This is usually calculated in a laboratory environment which is not practical for on-site biomechanical analysis. The validity of the proposed method, was assessed externally by comparing the results with a lab-based reference method and internally by comparing the estimated L5/S1 joint moments from top-down model and bottom-up model. It was shown that no significant differences in peak and mean moments between the two methods and intra-class correlation coefficients revealed excellent reliability of the proposed method (>0.91). The proposed method provides a reliable tool for assessment of lower back loads during occupational lifting and can be an alternative when the use of marker-based motion tracking systems is not possible.
authors.affiliation: Department of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ, USA.
authors.forename: Rahil
authors.initials: R
authors.lastname: Mehrizi
authors.affiliation: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA.
authors.forename: Xu
authors.initials: X
authors.lastname: Xu
authors.affiliation: Department of Computer Science, University of North Carolina, Charlotte, NC, USA.
authors.forename: Shaoting
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, NJ, USA.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Pavlovic
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, NJ, USA.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Metaxas
authors.affiliation: Department of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ, USA; Department of Computer Science, Rutgers University, Piscataway, NJ, USA; Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA; Department of Orthopaedics, Rutgers New Jersey Medical School, Newark, NJ, USA. Electronic address: kl419@soe.rutgers.edu.
authors.forename: Kang
authors.initials: K
authors.lastname: Li
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena; D064368:Healthy Volunteers; D006801:Humans; D007596:Joints / Q000502:physiology*; D017770:Lifting; D008159:Lumbar Vertebrae / Q000502:physiology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D015203:Reproducibility of Results; D012447:Sacrum / Q000502:physiology; D013647:Task Performance and Analysis; D016474:Weight-Bearing / Q000502:physiology*
keywords: Computer vision; Lifting; Marker-less motion capture