Within the MikroBeM project we investigate the impact of real micro- and hypergravity on fine motor and cognitive learning states, workload conditions, and movement kinematics, analyzing biological signals in particular electroencephalography (EEG), electromyography (EMG), and heart rate variability (HRV). The primary focus is to examine the motor learning effect under real microgravity. Results will be compared with existing and, in some cases, yet-to-be-collected data from simulated microgravity conditions. Astronauts are exposed to extreme workloads. During spacewalks, they are further constrained by bulky spacesuits, limiting movement and visibility. This necessitates the execution of fine motor movements even when arm or hand movements may be obscured. Currently, there are no investigations exploring movement kinematics and the learning effect for fine motor movements with concealed extremities under real microgravity, nor do any examine adaptation capabilities based on training status. Concealed extremities eliminate automatic corrections via the visual system and are of particular interest, as most (fine motor) actions are performed without visual contact. Existing studies only examine fine motor movement with visual contact, which represents an unrealistic condition for activities. However, results under realistic, controlled conditions (without visual contact) would be crucial for astronaut training prior to space missions, both to optimize training effectiveness and to better understand challenges in translating these movements into automated actions.
Therefore, the project aims to investigate to what extent the movement kinematics after training under simulated microgravity or without training under real microgravity differ. A key aspect of the analysis will be the examination of movement trajectories. A unique element will be that movements are concealed during execution, precluding visual correction; however, visual feedback will be provided in a later phase. Corrections based on visual feedback will manifest as abrupt changes in trajectory. These changes will be identified and evaluated using classical signal processing and machine learning techniques. Further, the learning effect in real microgravity will be investigated to determine how it changes compared to Earth gravity and simulated microgravity, and how it is altered after training under simulated microgravity. Real microgravity will be induced through parabolic flights, while simulated microgravity for training will utilize an active upper-body exoskeleton.
Beyond the effects of real microgravity, the project will also test a further development of the exoskeleton capable of simulating various gravitational conditions (e.g., Martian gravity, lunar gravity), thereby broadening the system’s applicability. Furthermore, research will investigate how both micro- and hypergravity can be actively compensated, enabling movements to be performed in Earth gravity during parabolic flights. Expected results will provide the foundation for developing specific, personalized training programs for astronauts to reduce training time through the comparatively cost-effective and widely applicable exoskeletons. Moreover, in line with a technology transfer to other application areas, MikroBeM contributes to the German Federal Government's Artificial Intelligence strategy in the field of AI in healthcare and nursing. Future projects can test whether the developed training and control scenarios can be transferred to neuromotor rehabilitation. This is unpredictable due to the high diversity of neurological conditions and would need to be tested in a patient study. Success in this area could lead to a significant expansion of therapeutic possibilities in neuromotor rehabilitation.
Partners
Universität Duisburg Essen - Systeme der Medizintechnik (Prof. Dr. Elsa Kirchner)
