Stroke survivors are often characterized by hemiparesis , i.e. , paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose novel measures of similarity and asymmetry in hand activities through bivariate Poincaré analysis between two-hand accelerometer data for quantifying hemiparetic severity. The proposed descriptors characterize the distribution of activity surrogates derived from acceleration of the two hands, on a 2D bivariate Poincaré Plot. Experiments show that while the descriptors $CSD1$ and $CSD2$ can identify hemiparetic patients from control subjects, their normalized difference $CSDR$ and the descriptors Complex Cross-Correlation Measure ( $C3M$ ) and Activity Asymmetry Index ( $AAI$ ) can distinguish between mild , moderate and severe hemiparesis. These measures are compared with traditional measures of cross-correlation and evaluated against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for hemiparetic severity estimation. This study, undertaken on 40 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( $< $ 5 minutes) wearable accelerometry data for identifying hemiparesis with greater clinical sensitivity. Results show that the proposed descriptors with a hierarchical classification model outperform state-of-the-art methods with overall accuracy of 0.78 and 0.85 for 4-class and 3-class hemiparesis identification respectively.

中文翻译：

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用于识别中风幸存者偏瘫严重程度的上肢活动相似性和不对称性的新测量

中风幸存者的特点通常是 偏瘫 , IE ，半身瘫痪，严重影响上肢运动。监测偏瘫的进展需要定期手动观察肢体运动，因此是一个劳动密集型过程。在这项工作中，我们使用腕戴式加速度计自动评估急性中风的偏瘫。我们提出了手部活动中相似性和不对称性的新措施，通过双变量庞加莱分析在用于量化偏瘫严重程度的两只手加速度计数据之间。提议的描述符表征了活动在二维二元庞加莱图中，从两只手的加速度派生的代理。实验表明，虽然描述符$CSD1$ 和 $CSD2$ 可以从对照受试者中识别偏瘫患者，他们的归一化差异 $CSDR$ 和描述符复杂互相关测量（ $C3M$ ) 和活动不对称指数 ( $AAI$ ) 可以区分 温和的 , 缓和 和 严重偏瘫。这些测量与传统的互相关测量进行比较，并根据美国国立卫生研究院卒中量表 (NIHSS) 进行评估，NIHSS 是偏瘫严重程度评估的临床金标准。这项研究对 40 名患有不同程度轻偏瘫的急性中风患者和 15 名健康对照者进行，验证了短长度的使用。 $< $ 5 分钟）可穿戴加速度计数据，用于以更高的临床灵敏度识别偏瘫。结果表明，所提出的具有分层分类模型的描述符优于最先进的方法，对于 4 级和 3 级偏瘫识别的总体准确度分别为 0.78 和 0.85。