The rapid growth of machine learning techniques has promoted innovative advancements across various domains, including health, medicine, and rehabilitation. However, the effectiveness of these methods heavily relies on the availability of large and well-annotated datasets. In the realm of medicine, a common challenge lies in the existence of extensive datasets, which often lack comprehensive labeling. This limitation hampers the progress and deployment of automated algorithms across various medical applications. This thesis addresses the challenge of unlabeled video datasets by proposing a novel approach for generating automatic labels in the context of monitoring the upper limb activity in stroke patients. Leveraging developments in deep learning and computer vision, the proposed framework extracts relevant features from video sequences and inputs them into Snorkel to generate labeled data of hand activity.