Unobtrusive Monitoring of Sensorimotor Performance in Ground-Based Functional Tasks

Astronauts returning from long-duration exposure to microgravity frequently exhibit alterations in sensorimotor function leading to postural imbalance, impaired locomotion, and operational challenges to manual control. Mission duration and individual responses often influence both the severity of performance decrements and the variability in adaptation timelines. Postflight disruptions during functional tasks are often detected through body-worn inertial measurement unit (IMU) devices. While IMU sensors are relatively compact, the long-term wear may lead to discomfort, displacement of the sensors on the body, and restrictions in movement or crew behavior. Although IMU data offers valuable insights from a research standpoint, interpreting changes in pre- and post-flight measures can be difficult for crew support personnel beyond the research domain, which can hinder the application to medical assessments and rehabilitation. Finally, the availability of inertial sensors in-flight is limited. There is a need for unobtrusive monitoring tools to improve our ability to monitor adaptation following gravitational transitions in various postflight evaluations and rehabilitation settings. Markerless motion capture is an evolving unobtrusive technology that builds upon decades of research with marker-based systems to provide 3D human pose estimation from multiple synchronized 2D camera views using deep learning algorithms. Markerless technology can revolutionize how data is captured pre- and post-flight and potentially in-flight during intravehicular activity by enabling pose estimation of multiple crew members from onboard camera hardware. The following paper presents an evaluation of a state-of-the-art commercial-off-the-shelf markerless motion capture system, Theia3D, compared to traditional IMU devices during various functional tasks and environmental conditions. Concurrent functional data are analyzed for nine participants of varying anthropometry. The analysis includes limited assessments of clothing, camera availability, and testing settings. The average RMSD was less than five degrees for all functional tasks evaluated in both a laboratory and field-testing location. The field-testing location, led to slightly higher deviations in head and trunk estimation. Functional tasks with more dynamic requirements resulted in larger deviations. The limited assessment of clothing indicates that the flight suit has minimal impact on the estimation accuracy of the head and torso. The development of visualization tools to enhance the application of the pose estimation output is also presented. These tools offer effective methods for anonymizing sensitive crew data, facilitating numerous applications across research, medical, and rehabilitation groups. The following work lays the foundation for future implementation leveraging markerless motion capture to assess the time course of recovery to baseline performance and provide insight for rehabilitation protocols to enhance crew readiness for the resumption of daily activities.