PDF(Pubmed)
Abstract：
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson\'s disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
摘要：
使用可穿戴传感器进行定量移动性分析，虽然有望作为帕金森病（PD）的诊断工具，在临床环境中不常用。主要障碍包括仪器移动测试和后续数据处理的最佳方案的不确定性，以及这个多步骤过程增加的工作量和复杂性。为了简化诊断PD时基于传感器的移动性测试,我们分析了262名PD参与者和50名对照者的数据,这些参与者在他们的下背部佩戴包含三轴加速度计和三轴陀螺仪的传感器,执行多项运动任务.使用异构机器学习模型的集合，其中包含在一组传感器特征上训练的一系列分类器，我们证明了我们的模型有效地区分了PD和对照的参与者，混合阶段PD(92.6%的准确率)和仅选择轻度PD的组(89.4%的准确率).省略复杂移动任务的算法分割降低了我们模型的诊断准确性，包括运动学特征也是如此。特征重要性分析显示，定时向上和去（TUG）任务贡献最高产量的预测特征，对于基于认知TUG作为单一移动性任务的模型，其准确性仅略有下降。我们的机器学习方法有助于简化仪器化移动性测试，而不会影响预测性能。
