Objective assessment of Parkinson’s disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.

日常生活中客观评估帕金森病症状有助于改善疾病管理并加速新疗法的开发。然而，当前的许多方法需要使用多种设备或执行规定的运动活动，这使得它们不适合自由生活条件。此外，缺乏能够证明该人群运动症状持续客观评估的标准有效性和判别有效性的开放方法。因此，需要一种能够通过使用最小的传感器设置来减轻患者负担的系统，同时持续捕获自由生活条件下运动症状严重程度的有临床意义的测量值。我们提出了一种方法，通过在分层框架中使用启发式和机器学习模型，顺序处理来自单个腕戴式加速度计的原始传感器数据，以提供对震颤和运动迟缓的连续监测。结果表明，传感器衍生的静息性震颤和运动迟缓的连续测量与症状严重程度的临床评估达到良好至强烈的一致性，并且能够区分与治疗相关的运动状态变化。