Fall Anxiety: Quantum State Probability as a Key Feature in Parkinson’s Risk Prediction

Abstract

Fear of falling remains a significant concern among individuals with Parkinson’s Disease (PD). Elevated scores on the Fall Efficacy Scale-International (FES-I) are associated with recurrent falls and anxiety that limits daily activities, ultimately increasing fall risk. While machine learning presents opportunities for fall risk prediction, the integration of psychological indicators and advanced feature engineering techniques, particularly for gait data, has not been thoroughly investigated. This study proposes quantum state probability values as novel features to predict fear of falling. Utilizing a motion capture dataset from the Federal University of ABC, Brazil, the model incorporates demographic data, L-Dopa dosage, and gait features. Among four dataset configurations, the model using only quantum state probability features achieved 93% accuracy with an SVM-RBF classifier. High Cohen’s Kappa and MCC values confirmed the strong predictive alignment with true labels. Visualization using PCA and t-SNE demonstrated less overlapping class separation in quantum state probability as feature.