Tien Iris, Glaser Steven D, Aminoff Michael J
Center for Information Technology Research in the Interest of Society (CITRIS), University of California, Berkeley, CA 94720, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3353-6. doi: 10.1109/IEMBS.2010.5627904.
Gait analysis is important in diagnosing and evaluating certain neurological diseases such as Parkinson's disease (PD). In this paper, we show the ability of our wireless inertial sensor system to characterize gait abnormalities in PD. We obtain physical features of pitch, roll, and yaw rotations of the foot during walking, use principal component analysis (PCA) to select features, and use the support vector machine (SVM) method to create a classification model. In the binary classification task of detecting the presence of PD by distinguishing between PD and control subjects, the model performs with over 93% sensitivity and specificity, and 97.7% precision. Using a cost-sensitive learner to reflect the different costs associated with misclassifying PD and control subjects, performance of 100% specificity and precision is achieved, while maintaining sensitivity of close to 89%. In the multi-class classification task of characterizing parkinsonian gait by distinguishing among PD with significant gait disturbance, PD with no significant gait disturbance, and control subjects, 91.7% class recall for control subjects is achieved and the model performs with 84.6% precision for PD subjects with significant gait disturbance. The features selected for this classification task indicate the features of gait that are principal in discriminating gait abnormalities due to PD compared to a normal gait. These results demonstrate the ability of our wireless inertial sensor system to successfully detect the presence of PD based on physical features of gait and to identify the specific features that characterize parkinsonian gait.
步态分析在诊断和评估某些神经系统疾病（如帕金森病（PD））中具有重要意义。在本文中，我们展示了我们的无线惯性传感器系统识别帕金森病步态异常的能力。我们获取了行走过程中足部俯仰、滚动和偏航旋转的物理特征，使用主成分分析（PCA）来选择特征，并使用支持向量机（SVM）方法创建分类模型。在通过区分帕金森病患者和对照受试者来检测帕金森病存在的二分类任务中，该模型的灵敏度和特异性超过93%，精度为97.7%。使用成本敏感学习器来反映将帕金森病患者和对照受试者误分类的不同成本，可实现100%的特异性和精度，同时保持接近89%的灵敏度。在通过区分有明显步态障碍的帕金森病患者、无明显步态障碍的帕金森病患者和对照受试者来表征帕金森步态的多分类任务中，对照受试者的类别召回率达到91.7%，对于有明显步态障碍的帕金森病患者，该模型的精度为84.6%。为该分类任务选择的特征表明，与正常步态相比，这些步态特征是区分帕金森病所致步态异常的主要特征。这些结果证明了我们的无线惯性传感器系统能够基于步态的物理特征成功检测帕金森病的存在，并识别出表征帕金森步态的特定特征。
