Wearable Sensor-Based Multi-modal Fusion Network for Automated Gait Dysfunction Assessment in Children with Cerebral Palsy

Abstract

Gait, fundamental to human movement, becomes compromised in cerebral palsy (CP), a childhood-onset central nervous system motor disorder. Precise assessment of patients’ gait is crucial for tailored rehabilitation interventions. Currently, clinical scales assessing CP gait dysfunction mostly, while valuable, rely on subjective clinician observations. To enhance objectivity and efficiency in CP diagnosis and rehabilitation, there is a need for more objective assessment procedures. This study introduces a multi-modal and multi-scale feature fusion (MMFF) framework, a new framework for automating gait dysfunction assessment in children with CP. By utilizing surface electromyography and acceleration signals recorded during children's walking, MMFF generates a feature vector enriched with adaptively refined feature maps, cross-mode correlations, and both local and global information. Validation of MMFF's effectiveness is evident through an accomplished classification accuracy of 99.13%. The mean values for precision, recall, and F1-score in Gross Motor Function Classification System (GMFCS)-1, GMFCS-2, and GMFCS-3, reaching 99.00%, 99.00%, and 98.33%, respectively, further reflect the accuracy of functional assessments at each level. This study underscores MMFF's potential as an objective, streamlined tool for clinicians, promising improved gait assessment and well-informed rehabilitation strategies for children with CP.