Bouchouras Georgios, Sofianidis Georgios, Kotis Konstantinos
Rehabilitation, School of Health Sciences, Metropolitan College, 54624 Thessaloniki, Greece.
Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece.
J Clin Med. 2025 Mar 20;14(6):2120. doi: 10.3390/jcm14062120.
: Freezing of gait (FoG) is a debilitating motor symptom of Parkinson's disease (PD), characterized by sudden episodes where patients struggle to initiate or sustain movement, often describing a sensation of their feet being "glued to the ground." This study investigates the potential of machine-learning (ML) models to predict FoG severity in PD patients, focusing on the influence of dopaminergic medication by comparing gait parameters in ON and OFF medication states. : Specifically, this study employed spatiotemporal gait features to develop a predictive model for FoG severity, leveraging a random forest regressor to identify the most influential gait parameters associated with this in each medication state. The results indicate that the model achieved higher predictive performance in the OFF-medication condition (R² = 0.82, MAE = 2.25, MSE = 15.23) compared to the ON-medication condition (R² = 0.52, MAE = 4.16, MSE = 42.00). : These findings suggest that dopaminergic treatment alters gait dynamics, potentially reducing the reliability of FoG predictions when patients are medicated. Feature importance analysis revealed distinct gait characteristics associated with FoG severity across medication states. In the OFF condition, step length parameters, particularly left step length mean, were the most dominant predictors, alongside swing time and stride width, indicating the role of spatial and temporal gait control in FoG severity without medication. In contrast, under the ON medication condition, stride width and gait speed emerged as the most influential predictors, followed by stepping frequency, reflecting how medication influences stability and movement rhythm. : These findings highlight the need for predictive models that account for medication-induced gait variability, ensuring more reliable FoG detection. By integrating spatiotemporal gait analysis and ML-based prediction, this study contributes to the development of personalized intervention strategies for PD patients experiencing FoG episodes.
冻结步态（FoG）是帕金森病（PD）的一种使人衰弱的运动症状，其特征是突然发作，患者难以启动或维持运动，常描述感觉自己的脚“粘在地上”。本研究调查了机器学习（ML）模型预测PD患者FoG严重程度的潜力，通过比较服药和未服药状态下的步态参数，重点关注多巴胺能药物的影响。具体而言，本研究采用时空步态特征来开发FoG严重程度的预测模型，利用随机森林回归器来识别每种药物状态下与FoG相关的最具影响力的步态参数。结果表明，与服药状态（R² = 0.52，平均绝对误差 = 4.16，均方误差 = 42.00）相比，该模型在未服药状态下具有更高的预测性能（R² = 0.82，平均绝对误差 = 2.25，均方误差 = 15.23）。这些发现表明，多巴胺能治疗会改变步态动力学，可能会降低患者服药时FoG预测的可靠性。特征重要性分析揭示了不同药物状态下与FoG严重程度相关的独特步态特征。在未服药状态下，步长参数，特别是左步长平均值，是最主要的预测指标，同时还有摆动时间和步幅宽度，表明在未服药时时空步态控制对FoG严重程度的作用。相比之下，在服药状态下，步幅宽度和步态速度成为最具影响力的预测指标，其次是步频，反映了药物对稳定性和运动节奏的影响。这些发现凸显了需要考虑药物引起的步态变异性的预测模型，以确保更可靠的FoG检测。通过整合时空步态分析和基于ML的预测，本研究为经历FoG发作的PD患者的个性化干预策略的发展做出了贡献。
