Identifying Parkinson's disease and its stages using static standing balance.

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 F-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.