The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients’ responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.

当人们考虑到临床研究通常仅报告部分参与者获得令人满意的运动增益时，显然需要制定针对患者的干预措施。这一观察结果为“精准康复”提供了基础。在这种情况下，跟踪和预测定义恢复轨迹的结果是关键。使用可穿戴传感器收集的数据为临床医生提供了这样做的机会，而对临床医生和患者的负担很小。本文提出的方法依赖于基于机器学习的算法，从功能性运动任务期间收集的可穿戴传感器数据中得出临床评分估计。基于传感器的评分估计与临床医生生成的评分非常一致。上肢损伤严重程度和运动质量的评分估计分别用 0.86 和 0.79 的确定系数来标记。应用所提出的方法来监测患者对康复的反应预计将有助于制定针对患者的干预措施，旨在最大限度地提高运动增益。