Heldman Dustin A, Filipkowski Danielle E, Riley David E, Whitney Christina M, Walter Benjamin L, Gunzler Steven A, Giuffrida Joseph P, Mera Thomas O
Great Lakes NeuroTechnologies Inc., Cleveland, OH 44125, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1956-9. doi: 10.1109/EMBC.2012.6346338.
The objective was to develop and evaluate algorithms for quantifying gait and lower extremity bradykinesia in patients with Parkinson's disease using kinematic data recorded on a heel-worn motion sensor unit. Subjects were evaluated by three movement disorder neurologists on four domains taken from the Movement Disorders Society Unified Parkinson's Disease Rating Scale while wearing the motion sensor unit. Multiple linear regression models were developed based on the recorded kinematic data and clinician scores and produced outputs highly correlated to clinician scores with an average correlation coefficient of 0.86. The newly developed models have been integrated into a home-based system for monitoring Parkinson's disease motor symptoms.
目的是利用足跟佩戴的运动传感器单元记录的运动学数据，开发并评估用于量化帕金森病患者步态和下肢运动迟缓的算法。在佩戴运动传感器单元时，由三位运动障碍神经科医生根据运动障碍协会统一帕金森病评定量表的四个领域对受试者进行评估。基于记录的运动学数据和临床医生评分建立了多元线性回归模型，模型输出与临床医生评分高度相关，平均相关系数为0.86。新开发的模型已集成到一个用于监测帕金森病运动症状的家庭系统中。
