Stroke survivors are often characterized by upper limb hemiparesis due to which activities in one of the hands is significantly restricted. Manual evaluation of the progression of hemiparesis in acute stroke patients involves 24x7 medical supervision, which is prone to inter-rater variability, is labor-intensive and consequently expensive in public hospitals. In this paper, we investigate the use of wrist-worn accelerometers for automated identification of upper limb hemiparesis in acute stroke. We propose a set of spontaneous and instructed movements in order to estimate two-hand activity correlation using accelerometry data. We use this information to determine the weak hand and further investigate an Activity Based Distance (ABD) measure to quantify this correlation. We compare ABD with standard time-series distance measures such as Lp norms and Dynamic Time Warping (DTW) for hemiparetic severity estimation. We study these distance measures with respect to the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard to determine hemiparetic severity, and demonstrate their suitability for developing a wearable based automated hemiparesis detection and monitoring system.Clinical relevance-This study presents a novel experimental paradigm for identifying upper limb hemiparesis in acute stroke patients using measures of two-hand activity correlation.