Activity Recognition (AR) in older adults living with Neurocognitive disorders caused by diseases such as Alzheimer’s is still a challenging research problem. The inherent natural variation in performing an activity increases while repeating the same activity for an older adult, let alone the variation introduced when another older adult performs the same activity. Moreover, the challenges in acquiring the labeled data while preserving the privacy, availability of annotators with domain knowledge, aversion towards cameras even for a minimal amount of time for ground truth data collection, and psychological and mental health status make AR for older adults challenging. In this paper, we postulate a self-taught learning-based approach that helps recognize activities with variations that are not being directly seen during the training phase. We hypothesize that the features extracted using deep architectures from unlabeled data instances can learn general underlying representations of activities efficiently and help improve activity classification in a supervised setting, although the data instances in labeled data do not follow the generative distribution of that of unlabeled data. We posit real data from a retirement community center using our in-house SenseBox infrastructure and survey-based assessments concurrently done by a clinical evaluator to study the relationship between activities and functional/behavioral health of older adults. We evaluate our proposed self-taught learning-based approach, STAR, using the presented in-house Alzheimer’s Activity Recognition (AAR) dataset acquired in a real-world deployment in 25 homes which outperforms the state-of-the-art algorithm by about 20%.