Understanding the role of internal movement errors in falling using a blended experimental and computational approach

You are craving a cup of coffee and start walking towards the coffee machine. Your movement is driven by a high-level goal (coffee), but every step towards it is filled with danger as lifting your foot to step makes you less stable. To avoid falling, your nervous system accounts for the upcoming instability by predicting the outcome of your movements and closely monitors the execution of the planned movement. One third of older adults fall each year, predominantly because of incorrect weight shifts during walking or stepping. Yet, researchers mostly investigate circumstances of falling due to external perturbations, e.g. slips and trips. Studying incorrect weight shifts during stepping is difficult for two reasons. First, incorrect weight shifts leading to falls only occur occasionally. Second, it is hard to distinguish the effect of different sources of internal errors related to movement planning, execution (motor noise), and monitoring (sensory noise) experimentally. We propose a blended experimental and computational approach to investigate the effect of internal errors on instability of step initiation. We will combine a novel experimental approach that mimics internal errors by movement-related perturbations with computer models that allow us to study the effect of isolated changes in movement planning, motor noise, and sensory noise on step initiation. The results of the proposed project will allow the design of better fall prevention strategies.