Rendering of a Local 1g Environment for Enhanced Motor Learning in Altered Gravity

Human motor adaptation is crucial to adapt to new environments, such as altered gravity. Dexterous manipulation and fine movements in space require learning new coordinated motor actions. Traditionally, adaptation mechanisms have been tested in laboratories with robotic devices that perturb specific task parameters unbeknownst to the participant. Over repetition, participants build a more accurate representation of the task dynamics and, eventually, improve performance. These perturbations are applied locally on the hand or limb while the dynamics of the rest of the body remains unaltered. These approaches are therefore limitative since they do not reflect ecological adaptation to globally changed dynamics, such as new gravitational environments. Parabolic flights, centrifuges and water immersion allow circumventing these limitations. Previous investigations in these contexts have highlighted the role of the global context in motor adaptation. However, it is unknown if global learning could benefit from exploiting known local dynamics. Here, we design an original task that will capture both the learning of arm movement kinematics as well as grasping forces for object manipulation in an ecologically valid design. We test whether executing this task in hypogravity with rendering of Earth gravity locally at the hand is beneficial or detrimental to task performance. By adopting the "negative picture" of conventional robotic approaches, these results will further our understanding of basic motor adaptation and provide insightful information on the optimal design and control of human-machine interfaces and wearable robots in space environments and other immersive dynamics.