Information and communication technology (ICT) plays a crucial role in developing tools to enhance efficiency and reduce uncertainty in health-related procedures. The widespread adoption of sensors has facilitated the generation of large amounts of data that can be exploited with advanced data processing techniques. This research studies depth cameras (3D cameras) to assist health professionals in assessing frailty. It proposes using depth cameras to generate a dataset that can be used to train machine learning algorithms to automatically evaluate balance tests (side-by-side stance, semi-tandem stance, and tandem stance) for frailty assessment. Non-frail individuals participated in performing the three balance tests. Virtual reality lenses were used to induce imbalanced behaviours in the participants, generating data representing both balanced and imbalanced behaviours during the tests. The article presents the methodology that underpins the generation of this dataset, including the tools employed to validate the adequate performance of the cameras and support the labelling process. The dataset will be made publicly available upon completion of the labelling.