ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Cervical myelopathy screening using in-shoe inertial measurement unit sensors and machine learning focusing on gait disturbance.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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15 authors.
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Abstract
purposeCervical myelopathy (CM) often presents with gait disturbances, which can lead to falls and spinal cord injury. As the presence of gait disturbances negatively affects postoperative improvement in patients with CM, early detection of gait disturbances is crucial. However, current gait analysis methods require specialisation, limiting their feasibility for screening. This study aimed to perform gait analysis in patients with CM using wearable in-shoe inertial measurement unit (IMU) sensors and to develop a screening system using machine learning.
methodsThis study included 26 patients with CM and 26 age-matched healthy controls. Gait parameters were measured using IMU sensors while participants walked along a 16 m corridor. Eleven gait parameters and their coefficients of variation (CV) were analysed. A machine learning model using the extreme gradient boosting algorithm was developed for CM classification.
resultsPatients with CM exhibited significantly lower gait speed, stride length, dorsiflexion angle, plantarflexion angle, and foot height, along with increased stance time and stance phase. CVs of gait parameters were higher in patients with CM, with an increased stance phase observed from the early stages. The classification model achieved high performance (AUC = 0.89, sensitivity = 81%, specificity = 85%), with stance phase and CVs of stride length and plantarflexion angle identified as key predictive features.
conclusionWearable IMU sensors effectively captured gait disturbances in CM patients, and machine learning enabled accurate classification. This method enables daily gait monitoring and could facilitate early detection and intervention for CM.
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