Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Prediction of Cobb-angle for Monitoring System in Adolescent Girls with Idiopathic Scoliosis using Multiple Regression Analysis

  • Seo, Eun Ji (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Choi, Ahnryul (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Oh, Seung Eel (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Park, Hyun Joon (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Lee, Dong Jun (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Mun, Joung H. (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University)
  • Received : 2012.10.18
  • Accepted : 2013.02.28
  • Published : 2013.03.01
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Abstract

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: $10^{\circ}-25^{\circ}$), and 14 severe scoliosis patients (Cobb angle: $25^{\circ}-50^{\circ}$) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement ($R^2$ = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately $3.2{\pm}1.8$. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.

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References

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