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

Sajad Ashouri, Mohsen Abedi, Masoud Abdollahi, Farideh Dehghan Manshadi, Mohamad Parnianpour, Kinda Khalaf

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)144-149
Number of pages6
JournalComputers in Biology and Medicine
Volume89
DOIs
StatePublished - 1 Oct 2017

Keywords

  • 3-D kinematics
  • Classification
  • Inertial senor
  • Low back pain
  • Pattern recognition

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