Poincaré descriptors for identifying hemiparesis in acute stroke using wearable accelerometry

Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, that severely affects upper limb movements. Continuous monitoring of the progression of hemiparesis requires manual observation of the limb movements at regular intervals and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparetic severity in acute stroke patients through bivariate Poincaré analysis between accelerometer data from the two hands during spontaneous and instructed movements. Experiments show that while the bivariate Poincaré descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, a novel descriptor called Complex Cross-Correlation Measure (C3M) can distinguish between moderate and severe hemiparesis. Further, we justify the use of C3M by showing that it is described by multiple-lag cross-correlations, representing the co-ordination of activity between two hands. The descriptors are compared against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for evaluation of hemiparetic severity, and studied using statistical tests for developing supervised models for hemiparesis classification.Clinical relevance-This study establishes the suitability of wrist-worn accelerometers in identifying hemiparetic severity in stroke patients through novel descriptors of hand co-ordination.