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Session: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00

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Brandon Rasman (Erasmus MC)
Jean-Sebastien Blouin (University of British Columbia)
Patrick Forbes (Erasmus MC)

Abstract:
Sensorimotor delays are a ubiquitous feature of movement control in all animals. Consequently, the brain must learn to control movement based on outdated sensory information and compensate for the influence a delay may have on volitional self-motion. Sensorimotor delays for human standing balance can be particularly long (up to 160 milliseconds) and are exacerbated through aging and neurological diseases. Failure to accommodate for these delays will result in instability and falls. Computational models of human balance have suggested that we cannot maintain upright balance with delays longer than 300-340 milliseconds. Using a custom-designed robotic balance simulator, we tested whether healthy volunteers could learn to balance with control delays longer than the predicted 300-340 millisecond limit. This unique device allowed us to artificially impose delays into the control of standing balance by increasing the time between the generation of motor signals and resulting whole-body motion. The experiments demonstrated that lengthening the sensorimotor delay initially destabilized upright standing, but through training, participants regained the ability to balance. We further tested whether humans can generalize this learning across different contexts (i.e., varying imposed delays, balancing in different directions, balancing with different muscle groups). After training in a single condition (i.e., single delay and balance direction), participants exhibited balance improvements in both trained and untrained contexts, demonstrating generalization of learning. Finally, a subset of participants was tested three months later and were still able to compensate for the increased delay, demonstrating learning retention. Our results reveal that while long delays are initially destabilizing for human standing, the brain can learn to overcome delays up to 560 milliseconds in the control of balance. Furthermore, the brain can generalize these learned control principles to balance upright across varying sensorimotor delays, movement directions and mechanically-independent motor effectors. Our findings may have important implications for people who develop balance problems due to older age or neurological diseases like diabetic neuropathy. It is possible that robot-assisted training therapies, like the one used in our study, could help people overcome their balance impairments.