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A novel approach to spinal 3-D kinematic assessment using inertial sensors: towards effective quantitative evaluation of low back pain in clinical settings

Ashouri, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.compbiomed.2017.08.002
  3. Abstract:
  4. This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate identification of LB patients. 24 healthy individuals and 28 low back pain patients performed trunk motion tasks in five different directions for validation. Four combinations of these motions were selected based on literature, and the corresponding kinematic data was collected. Upon filtering (4th order, low pass Butterworth filter) and normalizing the data, Principal Component Analysis was used for feature extraction, while Support Vector Machine classifier was applied for data classification. The results reveal that non-linear Kernel classification can be adequately employed for low back pain identification. Our preliminary results demonstrate that using a single inertial sensor placed on the thorax, in conjunction with a relatively simple test protocol, can identify low back pain with an accuracy of 96%, a sensitivity of %100, and specificity of 92%. While our approach shows promising results, further validation in a larger population is required towards using the methodology as a practical quantitative assessment tool for the detection of low back pain in clinical/rehabilitation settings. © 2017 Elsevier Ltd
  5. Keywords:
  6. Classification ; Butterworth filters ; Cost effectiveness ; Health ; Inertial navigation systems ; Pattern recognition systems ; Principal component analysis ; 3-D kinematics ; Healthy individuals ; Inertial senor ; Pattern recognition techniques ; Quantitative assessments ; Quantitative evaluation ; Support vector machine classifiers ; Classification (of information) ; Accelerometer ; Adult ; Biosensor ; Clinical article ; Clinical evaluation ; Controlled study ; Diagnostic accuracy ; Diagnostic test accuracy study ; Human ; Inertial sensor ; Kinematics ; Low back pain ; Male ; Pattern recognition ; Priority journal ; Quantitative analysis ; Sensitivity and specificity ; Support vector machine
  7. Source: Computers in Biology and Medicine ; Volume 89 , 2017 , Pages 144-149 ; 00104825 (ISSN)
  8. URL: https://www.sciencedirect.com/science/article/pii/S001048251730255X?via%3Dihub