Quantifying and modelling adaptive astronaut movement : motion strategies for long-duration spaceflight missions

Abstract

Past spaceflight experience has shown that astronauts adapt their motor control strategies to microgravity movements after approximately four weeks of microgravity exposure. A similar (but typically shorter) re-adaptation period is required upon return to Earth or partial gravity environment such as the Moon or Mars. During these adaptation periods, astronaut performance is considerably degraded and can lead to falls and mission-threatening injuries. This dissertation describes a research program to quantitatively study the dynamics and control aspects of human motor control adaptation to a spectrum of gravity environments. The key hypotheses of this research were that a) locomotor control adaptation could be observed following short exposure (on the order of hours) to a different dynamic environment and b) the observed adaptation could be predicted using a single model that applied to a spectrum of gravitational environments. Experiments were conducted on a 1-G air-bearing floor microgravity simulator and underwater to provide contrasting dynamic and gravitational environments. Subjects performed leg push-offs and hand landings to demonstrate their control strategies as they adapted.

(cont.) Forces and moments from the push-offs and landings were recorded using 6-axis force-moment sensors. Joint angles were measured using a kinematic video analysis system. A suite of dynamic estimation filters was written to combine the kinetic and kinematic data. Experimental results showed significant motor control adaptation to the air-bearing floor experiments, evidenced by reduced peak push-off forces and increased sensor contact times. A model based on Golgi tendon organ (GTO) force feedback was proposed to predict the observed adaptation. Comparisons between the experimental data and the model predictions indicate that the GTO adaptation model can adequately predict the observed adaptation.