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Upper-limb hemiparesis resulting from stroke is a common cause of long-term disability. Wearable inertial sensors offer a potential means of developing assessments of motor impairment severity that are more objective, ecologically valid, and that can be administered frequently than traditional clinical motor scales. Our recent work proposed a method for unobtrusively estimating upper-limb impairment severity by analyzing submovements extracted from the performance of large, continuous, random movements. Here, we validate that similar analytic methods are able to estimate upper-limb impairment severity from the performance of activities of daily living (ADLs) using only the data obtained from a single wrist-worn inertial sensor. Twenty stroke survivors were equipped with an nine-axis inertial sensor on the stroke-affected wrist and performed four ADLs that involved upper-limb movements and required manipulation of the environment. A random forest model trained on the kinematic features of submovements extracted from ADL performance was able to estimate the upper extremity portion of the Fugl-Meyer Assessment with a normalized root mean square error of 17.0% and R2 = 0.75. These results support the potential for a technology that can assess stroke survivors' real-world upper-limb motor performance in a seamless, minimally-obtrusive manner, though additional development and validation are needed to achieve this vision.