Stroke survivors usually experience paralysis in one half of the body, i.e., hemiparesis, and the upper limbs are severely affected. Continuous monitoring of hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length (<; 10 minutes) accelerometry data to identify hemiparesis through leave-one-subject-out cross-validation based hierarchical discriminant analysis. The results indicate that the proposed approach can distinguish between controls, moderate and severe hemiparesis with an average accuracy of 91%.

中文翻译：

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使用可穿戴加速度计测量上肢协调性对急性中风偏瘫进行自动评分


中风幸存者通常会出现半身瘫痪，即偏瘫，上肢受到严重影响。中风发作后数小时持续监测偏瘫进展，包括由医院医学专家手动观察上肢运动。因此，它是资源和时间密集型的，而且容易出现人为错误和评估者之间的差异。可穿戴设备在自动连续监测中风等神经系统疾病方面具有重要意义。在本文中，我们使用腕戴式设备获取的加速度计信号来分析急性中风患者的上肢运动并识别偏瘫，同时他们执行一组建议的自发和指导运动。我们提出了不同滞后（和频带）下两个臂的加速度计数据之间的时（和频）域一致性的新颖测量方法。这些测量值与测量中风偏瘫严重程度的临床黄金标准——美国国立卫生研究院中风量表 (NIHSS) 密切相关。该研究针对 32 名具有不同偏瘫程度的急性中风患者和 15 名健康对照者进行，验证了使用短长度（<；10 分钟）加速测量数据通过基于留一受试者排除交叉验证的分层方法来识别偏瘫。判别分析。结果表明，所提出的方法可以区分对照、中度偏瘫和重度偏瘫，平均准确率为 91%。