Cadence-Specific Gait Evaluation Using Synthetic Signals: A New Approach to Pathological Assessment

This study introduces an automated system to generate synthetic biomechanical data tailored to specific cadences, enabling differentiation between pathological deviations and natural cadence-based variations. By leveraging Gaussian Mixture Models (GMM) and a portable monitoring system, we generate cadence-specific healthy gait signals for objective comparisons. The proposed method was validated by comparing synthetic data to real-world average data, demonstrating its potential.Clinical Relevance- The clinical relevance lies in improving the accuracy and reliability of gait assessments, essential for diagnosing and managing musculoskeletal and neurological disorders. By assessing cadence-specific variations in gait, clinicians can differentiate between changes caused by natural physiological adaptations and those due to pathological compensations.