Derivation of the Gait Deviation Index for Spinal Cord Injury

Diana Herrera-Valenzuela; Isabel Sinovas-Alonso; Juan C Moreno; Ángel Gil-Agudo; Antonio J Del-Ama

doi:10.3389/fbioe.2022.874074

Derivation of the Gait Deviation Index for Spinal Cord Injury

Front Bioeng Biotechnol. 2022 Jul 6:10:874074. doi: 10.3389/fbioe.2022.874074. eCollection 2022.

Authors

Diana Herrera-Valenzuela^{1

2}, Isabel Sinovas-Alonso^{1

2}, Juan C Moreno³, Ángel Gil-Agudo², Antonio J Del-Ama⁴

Affiliations

¹ International Doctoral School, Rey Juan Carlos University, Madrid, Spain.
² Biomechanics and Technical Aids Unit, National Hospital for Paraplegics, Toledo, Spain.
³ Neural Rehabilitation Group, Cajal Institute, CSIC-Spanish National Research Council, Madrid, Spain.
⁴ School of Science and Technology, Department of Applied Mathematics, Materials Science and Engineering and Electronic Technology, Rey Juan Carlos University, Madrid, Spain.

Abstract

The Gait Deviation Index (GDI) is a dimensionless multivariate measure of overall gait pathology represented as a single score that indicates the gait deviation from a normal gait average. It is calculated using kinematic data recorded during a three-dimensional gait analysis and an orthonormal vectorial basis with 15 gait features that was originally obtained using singular value decomposition and feature analysis on a dataset of children with cerebral palsy. Ever since, it has been used as an outcome measure to study gait in several conditions, including spinal cord injury (SCI). Nevertheless, the validity of implementing the GDI in a population with SCI has not been studied yet. We investigate the application of these mathematical methods to derive a similar metric but with a dataset of adults with SCI (SCI-GDI). The new SCI-GDI is compared with the original GDI to evaluate their differences and assess the need for a specific GDI for SCI and with the WISCI II to evaluate its sensibility. Our findings show that a 21-feature basis is necessary to account for most of the variance in gait patterns in the SCI population and to provide high-quality reconstructions of the gait curves included in the dataset and in foreign data. Furthermore, using only the first 15 features of our SCI basis, the fidelity of the reconstructions obtained in our population is higher than that when using the basis of the original GDI. The results showed that the SCI-GDI discriminates most levels of the WISCI II scale, except for levels 12 and 18. Statistically significant differences were found between both indexes within each WISCI II level except for 12, 20, and the control group (p < 0.05). In all levels, the average GDI value was greater than the average SCI-GDI value, but the difference between both indexes is larger in data with greater impairment and it reduces progressively toward a normal gait pattern. In conclusion, the implementation of the original GDI in SCI may lead to overestimation of gait function, and our new SCI-GDI is more sensitive to larger gait impairment than the GDI. Further validation of the SCI-GDI with other scales validated in SCI is needed.

Keywords: gait deviation index (GDI); gait impairment; singular value decomposition; spinal cord injury (SCI); three-dimensional (3D) kinematic gait data; walking index for spinal cord injury (WISCI).