Recent advancements in machine learning have enabled the use of long-term accelerometry data collection and machine learning algorithms to quickly and accurately detect upper limb weakness. Although accelerometry-derived measurements are commonly used in long-term rehabilitation studies, this study aimed to determine whether similar techniques could be used to detect short-term changes in upper limb motor function in patients who were hospitalized soon after experiencing a stroke. Six binary classification models were created by training on variable data window times of paretic upper limb accelerometer feature data, and four preliminary visualizations were proposed to provide health professionals with information on the duration, intensity, symmetry, and variability of upper limb activity. The models were evaluated using Area Under the Curve (AUC) scores to classify the data into two classes: severe or moderately severe motor function. The AUC scores ranged from 0.72 to 0.94, with higher scores indicating better model performance. While this study provides a preliminary assessment of the efficacy of using accelerometry and machine learning to characterize upper limb motor function immediately following a stroke, the results suggest that further investigation is warranted.