Subjective clinical rating scales represent the gold-standard for diagnosis of motor function following stroke. In practice however, they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have not significantly impacted clinical practice. We address translational challenges in this arena through: (1) implementation of a novel sensor suite combining inertial measurement and mechanomyography (MMG) to quantify hand and wrist motor function; and (2) introduction of a new range of signal features extracted from the suite to supplement predicted clinical scores. The wearable sensors, signal features, and machine learning algorithms have been combined to produce classified ratings from the Fugl-Meyer clinical assessment rating scale. Furthermore, we have designed the system to augment clinical rating with several sensor-derived supplementary features encompassing critical aspects of motor dysfunction (e.g. joint angle, muscle activity, etc.). Performance is validated through a large-scale study on a post-stroke cohort of 64 patients. Fugl-Meyer Assessment tasks were classified with 75% accuracy for gross motor tasks and 62% for hand/wrist motor tasks. Of greater import, supplementary features demonstrated concurrent validity with Fugl-Meyer ratings, evidencing their utility as new measures of motor function suited to automated assessment. Finally, the supplementary features also provide continuous measures of sub-components of motor function, offering the potential to complement low accuracy but well-validated clinical rating scales when high-quality motor outcome measures are required. We believe this work provides a basis for widespread clinical adoption of inertial-MMG sensor use for post-stroke clinical motor assessment.

中文翻译：

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使用可穿戴惯性和肌力传感器的新颖组合对中风后运动功能进行量化


主观临床评定量表代表了诊断中风后运动功能的金标准。然而在实践中，它们受到公认的局限性，包括评估者方差、评估者间可靠性低和分辨率低。自动化系统已被提议用于经验量化，但尚未对临床实践产生重大影响。我们通过以下方式解决这一领域的转化挑战：（1）实施结合惯性测量和肌力描记（MMG）的新型传感器套件来量化手和腕部运动功能； (2)引入从该套件中提取的一系列新信号特征以补充预测的临床评分。可穿戴传感器、信号特征和机器学习算法相结合，根据 Fugl-Meyer 临床评估评级量表生成分类评级。此外，我们设计了该系统，通过几个传感器衍生的补充功能来增强临床评级，这些功能涵盖运动功能障碍的关键方面（例如关节角度、肌肉活动等）。通过对 64 名中风后患者进行的大规模研究验证了其性能。 Fugl-Meyer 评估任务的粗大运动任务分类准确率为 75%，手/手腕运动任务分类准确率为 62%。更重要的是，补充特征证明了与 Fugl-Meyer 评级的同时有效性，证明了它们作为适合自动评估的运动功能新测量方法的实用性。最后，补充功能还提供了运动功能子组件的连续测量，当需要高质量的运动结果测量时，提供了补充低准确度但经过充分验证的临床评级量表的潜力。 我们相信这项工作为临床广泛采用惯性 MMG 传感器进行中风后临床运动评估奠定了基础。