Hoffman Jeffrey D, McNames James
Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4378-81. doi: 10.1109/IEMBS.2011.6091086.
Functional motor impairment caused by Parkinson's disease and other movement disorders is currently measured with rating scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). These are typically comprised of a series of simple tasks that are visually scored by a trained rater. We developed a method to objectively quantify three upper extremity motor tasks directly with a wearable inertial sensor. Specifically, we used triaxial gyroscopes and adaptive filters to quantify how predictable and regular the signals were. We found that simply using the normalized mean squared error (NMSE) as a test statistic permitted us to distinguish between subjects with and without Parkinson's disease who were matched for age, height, and weight. A forward linear predictor based on the Kalman filter was able to attain areas under the curve (AUC) in receiver operator characteristic (ROC) curves in the range of 0.76 to 0.83. Further studies and development are warranted. This technology has the potential to more accurately measure the motor signs of Parkinson's disease. This may reduce statistical bias and variability of rating scales, which could lead to trials with fewer subjects, less cost, and shorter duration.
帕金森病和其他运动障碍所导致的功能性运动障碍目前是通过诸如统一帕金森病评定量表（UPDRS）等评定量表来测量的。这些量表通常由一系列简单任务组成，由经过培训的评估者进行视觉评分。我们开发了一种方法，可使用可穿戴惯性传感器直接客观地量化三项上肢运动任务。具体而言，我们使用三轴陀螺仪和自适应滤波器来量化信号的可预测性和规律性。我们发现，仅使用归一化均方误差（NMSE）作为检验统计量就能让我们区分年龄、身高和体重相匹配的帕金森病患者和非帕金森病患者。基于卡尔曼滤波器的前向线性预测器能够在接收器操作特征（ROC）曲线中获得曲线下面积（AUC），范围在0.76至0.83之间。有必要进行进一步的研究和开发。这项技术有可能更准确地测量帕金森病的运动体征。这可能会减少评定量表的统计偏差和变异性，从而可能使试验所需的受试者数量更少、成本更低且持续时间更短。
