Autonomous longitudinal functional health assessment is critically important to support the rehabilitation of older adults in Skilled Nursing Facilities (SNFs). Although the wide availability of commodity smarthome sensors and internet-of-things (IoT) is facilitating continual monitoring of individuals' health-related vital signs and behaviors, missing values, presence of multi-inhabitants and diversity of smarthomes interfere with successful longitudinal assessment and impact the scalability of autonomous health assessments. In this paper, we propose a novel scalable framework to provide health assessments of older adults living in varied smarthome environments. As a critical first step, we propose a novel algorithm to track individuals in a multi-inhabitant smarthome environment. We then propose a novel data curation technique to address missing sensor signals in a multi-modal ambient sensor-assisted environment. Finally, we propose a novel trajectory featurization method inspired Deep Convolutional Neural Network TDCNN, leveraging appropriate samples from a well-labeled source smarthome, to transfer functional health assessment knowledge to unlabeled diverse smarthomes, boosting the scalability of autonomous health assessment. Our evaluation on real SNFs data, collected over 5 months from 95 individuals residing in 9 diverse sensored SNFs environments shows promising results (93% accuracy) with respect to the scalability of our framework.