Personalized Prediction of Gait Freezing Using Dynamic Mode Decomposition

Freezing of gait (FOG) is a common severe gait disorder in patients with advanced Parkinson's disease (PD). The ability to predict the onset of FOG episodes early on allows for timely intervention, which is essential for improving the life quality of patients. Machine learning and deep learning, the current methods, face real-time diagnosis challenges due to comprehensive data processing requirements. Their ''black box'' nature makes interpreting features and classification boundaries difficult. In this manuscript, we explored a dynamic mode decomposition (DMD)-based approach together with optimal delay embedding time to reconstruct and predict the time evolution of acceleration signals, and introduced a triple index based on DMD to predict and classify FOG. Our predictive analysis shows 86.45% accuracy in classification, and an early prediction rate of 81.97% of all samples with an average early prediction time of 6.13 seconds. This DMD-based approach has the potential for real-time patient specific FOG prediction.