One of the most severe health risks to elderly populations is the risk of falling. Falls can lead to acute injuries, correlating with a decline in physical health. Recognizing the significance of this issue, the Fall Prevention Project aims to develop a method that can accurately predict and prevent falls in elderly demographics. Data collection is essential to understanding and predicting falls. This article addresses the data collection methods used in the Fall Prevention Project. For this project, two separate fall detection methods were proposed. The first method uses a gyroscope and accelerometer sensor embedded in a watch-like device to record data. The second uses a depth-camera-based device to record pose data. By collecting information to train a machine learning algorithm, patient stability can be modelled to reduce the likelihood of injuries and fatalities in at-risk patients. Recently, notable advancements have taken place in the development of prototypes for both the software and hardware components of the wearable and camera-based devices utilized in the Fall Prevention Project. The findings of this study contribute to the field of fall detection, offering insights into the advantages of camera-based analysis of human pose and gyroscope-based analysis of human pose, and improving the safety of individuals at risk of falls. Future work should include the integration of machine learning techniques to enhance system performance. The technology showcased in this study has implications for healthcare, monitoring, and elderly care, providing valuable insights to support various domains and improve quality of life.