Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% (r2=0.70) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors' rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.

中文翻译：

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使用可穿戴的惯性传感器和最少的繁琐的运动任务来估计中风幸存者的上肢损伤程度。

上肢轻瘫是中风后最常见的运动障碍。当前用于自动评估上肢损伤的解决方案给患者及其护理人员带来了许多关键负担，从而无法进行频繁评估。在这项工作中，我们提出了一种方法，使用两个可穿戴的惯性传感器（腕部和胸骨）和最小的运动任务来估计中风幸存者的上肢损伤。23名没有，轻度或中度上肢功能障碍的中风幸存者进行了两次重复一到两分钟的连续，随机（即无模式），自愿的上肢运动，这些运动遍及整个运动范围。通过采用独特的运动分解技术，将上肢运动的三维时间序列细分为一系列一维子运动。基于从这些子运动提取的特征，使用了无监督的聚类算法和监督的回归模型来估计Fugl-Meyer评估（FMA）分数。我们的回归模型估算的FMA分数具有18.2％的均方根误差（r2 = 0.70），并且仅需要一分钟的运动数据即可得出合理的估算性能。这些结果支持了经常监测中风幸存者康复结果的可能性，最终使制定个性化的康复计划成为可能。70），只需要短短一分钟的移动数据即可得出合理的估算效果。这些结果支持了经常监测中风幸存者康复结果的可能性，最终使制定个性化的康复计划成为可能。70），只需要一分钟的移动数据即可得出合理的估算效果。这些结果支持了经常监测中风幸存者康复结果的可能性，最终使制定个性化的康复计划成为可能。