Dattola Serena, Ielo Augusto, Quartarone Angelo, De Cola Maria Cristina
IRCCS Centro Neurolesi Bonino-Pulejo, S.S. 113 Via Palermo, C. da Casazza, 98124 Messina, Italy.
Bioengineering (Basel). 2025 Jan 6;12(1):37. doi: 10.3390/bioengineering12010037.
Tremor is one of the most common symptoms of Parkinson's disease (PD), assessed using clinician-assigned clinical scales, which can be subjective and prone to variability. This study evaluates the potential of unsupervised learning for the classification and assessment of tremor severity from wearable sensor data. We analyzed 25 resting tremor signals from 24 participants (13 PD patients and 11 controls), focusing on motion intensities derived from accelerometer recordings. The k-means clustering algorithm was employed, achieving a classification accuracy of 76% for tremor versus non-tremor states. However, performance decreased for multiclass tremor severity classification (57.1%) and binary classification of severe versus mild tremor (71.4%), highlighting challenges in detecting subtle intensity variations. The findings underscore the utility of unsupervised learning in enabling scalable, objective tremor analysis. Integration of such models into wearable systems could improve continuous monitoring, enhance rehabilitation strategies, and support standardized clinical assessments. Future work should explore advanced algorithms, enriched feature sets, and larger datasets to improve robustness and generalizability.
震颤是帕金森病（PD）最常见的症状之一，通常使用临床医生指定的临床量表进行评估，这种评估可能具有主观性且容易出现差异。本研究评估了无监督学习从可穿戴传感器数据中对震颤严重程度进行分类和评估的潜力。我们分析了来自24名参与者（13名PD患者和11名对照）的25个静息震颤信号，重点关注加速度计记录得出的运动强度。采用了k均值聚类算法，震颤与非震颤状态的分类准确率达到了76%。然而，多类震颤严重程度分类（57.1%）和重度与轻度震颤的二元分类（71.4%）的性能有所下降，这凸显了检测细微强度变化方面的挑战。研究结果强调了无监督学习在实现可扩展、客观的震颤分析方面的实用性。将此类模型集成到可穿戴系统中可以改善连续监测、加强康复策略并支持标准化临床评估。未来的工作应探索先进算法、丰富的特征集和更大的数据集，以提高稳健性和通用性。
