Timmermans Nienke A, Terranova Roberta, Soriano Diogo C, Cagnan Hayriye, Raykov Yordan P, Bucur Ioan Gabriel, Bloem Bastiaan R, Helmich Rick C, Evers Luc J W
Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Department of Medical, Surgical Sciences and Advanced Technologies G.F. Ingrassia, University of Catania, Catania, Italy.
NPJ Parkinsons Dis. 2025 Jul 10;11(1):205. doi: 10.1038/s41531-025-01056-2.
Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson's Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.
可穿戴传感器能够客观、持续地监测帕金森病（PD）患者的日常生活震颤情况。我们开发了一种用于PD震颤现实生活监测的开源算法，该算法在不同的腕戴式设备上均能实现通用性能。我们通过将两个独立的互补数据集进行独特组合来实现这一点。第一个数据集规模较小，但在家庭非脚本化活动期间收集了大量视频标注的陀螺仪数据集（24例PD患者；24例对照）。我们用这个数据集训练并验证了基于倒谱系数的逻辑回归震颤检测器。第二个数据集规模较大，是一个无监督数据集（517例PD患者；50例对照，使用不同设备收集了2周的数据），用于对该算法进行外部验证。结果表明，我们的算法能够可靠地量化现实生活中的PD震颤（灵敏度为0.61（0.20），特异性为0.97（0.05））。每周汇总的震颤时间和功率显示出良好的重测信度，并且与MDS-UPDRS静止性震颤评分具有中度相关性。这为利用可穿戴技术支持临床试验和个体震颤管理开辟了可能性。
