Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach

JOURNAL OF BIOMECHANICS

卷:118

Article

Jimenez-Grande, David¹  Atashzar, S. Farokh²  Martinez-Valdes, Eduardo¹  De Nunzio, Alessandro Marco³  Falla, Deborah¹ 

[1] Univ Birmingham, Sch Sport Exercise & Rehabil Sci, Ctr Precis Rehabil Spinal Pain CPR Spine, Birmingham B15 2TT, W Midlands, England

[2] NYU, Elect & Comp Engn, Mech & Aerosp Engn, New York, NY 10003 USA

[3] LUNEX Int Univ, Dept Exercise & Sports Sci, Differdange, Luxembourg

关键词: Machine learning;    Gait kinematics;    Biomarkers;    Chronic neck pain;   

DOI  :  10.1016/j.jbiomech.2020.110190

来源: Elsevier

【 摘 要 】

People with chronic neck pain (CNP) often present with altered gait kinematics. This paper investigates, combines, and compares the kinematic features from linear and nonlinear walking trajectories to design supervised machine learning models which differentiate asymptomatic individuals from those with CNP. For this, 126 features were extracted from seven body segments of 20 asymptomatic subjects and 20 individuals with non-specific CNP. Neighbourhood Component Analysis (NCA) was used to identify body segments and the corresponding significant features which have the maximum discriminative power for conducting classification. We assessed the efficacy of NCA combined with K-Nearest Neighbour (K NN), Support Vector Machine and Linear Discriminant Analysis. By applying NCA, all classifiers increased their performance for both linear and nonlinear walking trajectories. Notably, features selected by NCA which magnify the classification power of the computational model were solely from the head, trunk and pelvis kinematics. Our results revealed that the nonlinear trajectory provides the best classification performance through the NCA-K-NN algorithms with an accuracy of 90%, specificity of 100% and sensitivity of 83.3%. The selected features by NCA are introduced as key biomarkers of gait kinematics for classifying non-specific CNP. This paper provides insight into changes in gait kinematics which are present in people with non-specific CNP which can be exploited for classification purposes. The result highlights the importance of curvilinear gait kinematic features which potentially could be utilized in future research to predict recurrent episodes of neck pain. ? 2021 Elsevier Ltd. All rights reserved.

【 授权许可】

Free