Gait analysis and prediction in Parkinson's disease using rhythmic auditory stimulation: A data-driven approach with machine learning models.
Gait analysis and prediction in Parkinson's disease using rhythmic auditory stimulation: A data-driven approach with machine learning models.
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CN · Author affiliation · country only
Department of Rehabilitation Medicine, Fujian Provincial Geriatric Hospital, Fuzhou, Fujian, China. Electronic address: 1027336301@qq.com.Location evidence
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Original abstract
BACKGROUND: Gait disturbance is a core feature of Parkinson's disease (PD), contributing to mobility loss and fall risk. Rhythmic auditory stimulation (RAS) can improve gait, yet response varies across patients. This study evaluated RAS effects on gait and developed a model to predict responders. METHODS: Three hundred PD patients were enrolled and assigned to RAS or control groups. Gait data were collected with a 3D motion capture system and force plates, and baseline assessments included UPDRS-III and MoCA score. Clinical and gait features (stride length, speed, swing time, double support) were analyzed. Paired analyses compared pre- and post-RAS changes. Machine learning models (Random Forest, XGBoost, SVM) were trained on demographic and baseline gait data to predict RAS responsiveness, defined as improvement in gait speed and double support time. FINDINGS: Among the 171 patients in the RAS group, 116 (67.8 %) were identified as responders based on improvements in gait speed and double support time.The RAS group showed significant improvements in gait speed (+0.14 m/s), stride length (+0.10 m), and reductions in double support (-0.026 s) and variability (-0.012), all p < 0.001. Among models, Random Forest classifier demonstrated the best balanced performance (AUC = 0.713, accuracy = 0.71, F1 = 0.64, recall = 0.87), outperforming SVM and XGBoost models. Feature importance highlighted UPDRS-III, stride length, MoCA score, and gait asymmetry as key predictors. INTERPRETATION: RAS significantly enhances gait performance in PD, though with individual variability. A data-driven machine learning framework enables reasonable prediction of responders, supporting personalized gait rehabilitation strategies in clinical practice.