Monitoring Astronaut Performance Using Robotic Exoskeletons

The first aim of this research was to i) quantify the neuromuscular modulations that occur in response to wearing a conventional Phase VI EVA space suit glove (SSG) during a fatiguing task and ii) determine the efficacy of the Space Suit RoboGlove (SSRG), a robotic grasp assist device, in reversing undesirable neuromuscular modulations and restoring altered muscular activity to barehanded levels. Trends in amplitude and spectral distributions of the surface electromyography (sEMG) signals were used to derive metrics quantifying neuromuscular effort and fatigue. Results showed that by augmenting finger flexion, the SSRG successfully reduced the neuromuscular effort needed to close the fingers of the space suit glove in more than half of subjects during the fatiguing tasks. However, the SSRG required more neuromuscular effort to extend the fingers compared to a conventional SSG in many subjects. Psychologically, the SSRG aided subjects in feeling less fatigued during short periods of intense work compared to the SSG. The results of this study reveal the promise of the SSRG as a grasp-assist device that can improve astronaut performance and reduce the risk of injury by offsetting neuromuscular effort. These findings will enhance the understanding of astronaut-spacesuit interaction and provide direction toward designing improved spacesuit gloves and robotic-assist devices, such as the SSRG. To overcome the drawbacks of relying upon conventional metrics of quantifying fatigue using sEMG only (described above), the second objective of this research aimed to develop a novel system-based methodology for monitoring fatigue-induced performance degradation of the neuromusculoskeletal system. Autoregressive moving average models with exogenous inputs (ARMAX) were used to characterize the dynamics of the NMS system and track its changes over time. This modeling approach was an improvement to the sEMG-only approach of the first aim, because it captured the input-output relationship of sEMG signals with either kinematic or kinetic movement outputs. Results showed that the Freshness Similarity Index (FSI) clearly characterized the rate at which the human performance degraded over time and succinctly represented the manifestation of fatigue. The trends in FSI exhibited consistency across multiple days of testing, the resolving power to distinguish differences in prescribed work load and glove features, and the ability to identify the accumulation of residual fatigue from consecutive trials of a task. With impending plans for planetary and lunar explorations on the horizon, tracking an astronaut’s physiological status will be essential for ensuring mission safety and success. Thus, the presented methodology, which successfully demonstrated the capacity to detect changes in an individual’s performance while wearing a spacesuit glove, could be extended to assess full body health status in real-time, identify the onset of abnormal or degraded performance, and evaluate fitness-for-duty during manned space exploration. The third objective of this research sought to i) expand the aforementioned ARMAX model into a multivariate vectorial ARMAX (VARMAX) that utilizes sEMG and both kinematic and kinetic movement outputs and ii) analyze the validity and reliability of the FSI metric in quantifying fatigue. To accomplish these goals, an elbow exoskeleton was developed and programmed to resist human motion, thereby inducing fatigue, during a repetitive hammer curl exercise. Kinematic, kinetic, and sEMG data were collected synchronously during human subject experiments (ongoing) to monitor fatigue and evaluate the performance of the VARMAX model.