Fall or not fall – Better to know beforehand (and do something about it)
Abstract
Falls are a significant health threat among the aging population, leading to various injuries and negatively impacting mobility, socialization, and quality of life. Falls and the fear of falling often trigger a detrimental cycle: fear leads to reduced mobility, which accelerates neuromotor degeneration, thereby increasing fall risk and reinforcing fear. Our course will follow three main themes on falls: predicting, preventing, scaling.
To break the vicious cycle of falling, individuals at high risk for falls must be identified for intervention. Current approaches to fall risk assessment and fall prediction, specifically concerning their strengths and weaknesses, will be presented and discussed.
For people at risk of falling, perturbation-based balance training (PBT) has proven effective in increasing resilience to perturbations. However, the mechanisms, as well as the optimal dose, frequency, magnitude, and direction of perturbation, are not yet fully understood. Preliminary results on the effects of multidirectional perturbations during walking, conducted using the Computer Assisted Rehabilitation Environment system (CAREN - Motekmedical.com), will be discussed.
Finally, we will discuss the scaling of fall prevention training systems to address the general population in a timely and cost-effective manner. To support this, we are testing low-cost cable-based robotic devices designed to deliver pelvic perturbations on commercial treadmills. We provide a practical demonstration to showcase one of these systems and discuss sustainability and impact of deployment strategies.
Recommended literature
McCrum, C., Bhatt, T. S., Gerards, M. H. G., Karamanidis, K., Rogers, M. W., Lord, S. R., & Okubo, Y. (2022). Perturbation-based balance training: Principles, mechanisms and implementation in clinical practice. Frontiers in Sports and Active Living, 4. https://doi.org/10.3389/fspor.2022.1015394
Tan, G. R., Raitor, M., & Collins, S. H. (2020). Bump’em: An open-source, bump-emulation system for studying human balance and gait. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 9093-9099). IEEE. https://doi.org/10.1109/ICRA40945.2020.9197105