The current assessment of Parkinson’s disease (PD) relies on dynamic motor tasks, limiting accessibility. This study aimed to propose an innovative approach to identifying PD and its stages using static standing balance and machine learning. A total of 210 participants were recruited, including a control group and five PD groups categorized by stage. Each participant completed a 10-s static standing balance task in which center of pressure trajectory data in the medial-lateral and anterior-posterior directions were collected. Features were extracted from these trajectory data and the data derived from them using both representation learning and handcrafting methods. A Transformer encoder-based classifier was trained on these features and achieved an F₁-score of 0.963 in classifying the six study groups. This approach enhances the accessibility of PD assessment, enabling earlier detection and timely intervention. The novel data mining framework introduced in this study heralds a new era of time-series data-driven digital healthcare.

目前对帕金森病 （PD） 的评估依赖于动态运动任务，限制了可及性。本研究旨在提出一种使用静态站立平衡和机器学习来识别 PD 及其阶段的创新方法。共招募了 210 名参与者，包括 1 例对照组和 5 例按分期分类的 PD 组。每个参与者完成一项 10 秒的静态站立平衡任务，其中收集了内侧和前后方向的压力轨迹中心数据。特征是从这些轨迹数据中提取的，并使用表示学习和手工制作方法从中得出的数据。基于 Transformer 编码器的分类器对这些特征进行了训练，并在对六个研究组进行分类时取得了 0.963 的 F₁ 分数。这种方法提高了 PD 评估的可及性，可实现早期发现和及时干预。本研究中引入的新型数据挖掘框架预示着时间序列数据驱动型数字医疗保健的新时代。