The Ageing Process with a focus on Falls Prevention

By analyzing diverse lived experience data from those participating in fall prevention programs, valuable insights into the risk factors and patterns surrounding falls can be gained, enabling AI algorithms to identify predictive indicators and tailor fall prevention initiatives that cater to individuals' unique needs and circumstances. These targeted programs may include personalized exercise regimens, home modifications, and educational resources, all aimed at promoting greater safety and independence for at-risk individuals. Furthermore, the aggregation of data from these participants in ongoing fall prevention programs will fuel research efforts, providing valuable insights into program effectiveness and identifying areas for improvement, thus advancing our understanding of fall-related risks in aging populations.

Leveraging the power of AI and lived experience data in this iterative approach to fall prevention will not only create a safer environment for aging individuals but also foster continuous improvement, leading to a more impactful and sustainable impact on reducing fall risks and enhancing the overall well-being of the aging population.
We are presently working with a leading researcher in Australia on developing several projects.