Parkinson’s disease (PD) increases fall risk, leading to injuries and reduced quality of life. Accurate fall risk assessment is crucial for effective care planning. Traditional assessments are subjective and time-consuming, while recent assessment methods based on wearable sensors have been limited to 1-year follow-ups. This study investigated whether a short sensor-based assessment could predict falls over up to 5 years. Data from 104 people with PD without prior falls were collected using six wearable sensors during a 2-min walk and a 30-s postural sway task. Five machine learning classifiers analysed the data. The Random Forest classifier performed best, achieving 78% accuracy (AUC = 0.85) at 60 months. Most models showed excellent performance at 24 months (AUC > 0.90, accuracy 84–92%). Walking and postural variability measures were key predictors. Adding clinicodemographic data, particularly age, improved model performance. Wearable sensors combined with machine learning can effectively predict fall risk, enhancing PD management and prevention strategies.

帕金森病 （PD） 会增加跌倒风险，导致受伤和生活质量下降。准确的跌倒风险评估对于有效的护理计划至关重要。传统的评估是主观且耗时的，而最近基于可穿戴传感器的评估方法仅限于 1 年的随访。本研究调查了基于传感器的简短评估是否可以预测长达 5 年的跌倒。在 2 分钟的步行和 30 秒的姿势摆动任务期间，使用 6 个可穿戴传感器收集了 104 名既往无跌倒的 PD 患者的数据。五个机器学习分类器分析了数据。随机森林分类器表现最佳，在 60 个月时达到 78% 的准确率 （AUC = 0.85）。大多数模型在 24 个月时表现出优异的性能 （AUC > 0.90，准确率 84-92%）。步行和姿势变异性测量是关键预测因素。添加临床人口统计数据，尤其是年龄，提高了模型性能。可穿戴传感器与机器学习相结合，可以有效预测跌倒风险，增强 PD 管理和预防策略。