Wearable gait analysis of Cervical Spondylotic Myelopathy patients by fusing bipedal inertial information

Abstract

Cervical Spondylotic Myelopathy (CSM) is a degenerative disorder caused by cervical spinal cord compression, resulting in neurological impairment that disrupts motor function and leads to gait disturbances. Refined gait analysis through parameter quantification and multi-level feature fusion is essential for advancing precision medicine and rehabilitation research for CSM. This paper explores portable gait analysis for CSM patients using inertial sensors to enable multi-level analysis via bipedal information fusion. An adaptive threshold-based gait phase segmentation method is proposed, allowing zero-velocity update-aided spatial parameter calculation, with results consistent across optical laboratory and practical test. Using high-precision spatiotemporal parameters and movement intensity features, we further analyzed gait rhythm, stability, and symmetry, introducing an improved EWT-based indicator for rhythm and symmetry. Finally, statistical analysis and machine learning-based feature ranking of CSM gait characteristics were performed, accompanied by a detailed discussion on feature types. The results underscore the critical role of fused features in capturing CSM gait patterns, offering a valuable reference for comprehensive gait analysis for CSM patients.