Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson’s Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson’s Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.

可穿戴设备提供了追踪神经系统疾病的运动症状的潜力。运动数据与机器学习算法结合使用可以准确识别患有运动障碍的人及其运动症状的严重程度。在这项研究中，我们的目的是确定可穿戴传感器数据和机器学习算法与自动特征选择的结合是否可以估计帕金森病患者的临床评分量表，以及是否可以纵向监测运动症状进展。74 名患者每隔 3 个月访问实验室 7 次。使用六个惯性测量单元传感器记录他们的行走（2 分钟）和姿势摇摆（30 秒，闭眼）。简单线性回归和随机森林算法与自动特征选择或因式分解的不同例程一起使用，产生七种不同的机器学习算法来估计临床评级量表（运动障碍协会-统一帕金森病评级量表第三部分；MDS-UPDRS-三）。发现有 29 个特征在群体水平上随着时间的推移而显着进步。随机森林模型显示了七个模型中对 MDS-UPDRS-III 的最准确估计。与第一次就诊相比，模型估计检测到 15 个月内运动症状有统计学上显着的进展，而 MDS-UPDRS-III 没有捕捉到任何变化。可穿戴传感器和机器学习可以比传统使用的临床评定量表更好地跟踪帕金森病患者的运动症状进展。本研究中描述的方法可以与临床评定量表互补，以提高诊断和预后的准确性。