Ergonomic Risk Identification for Spacesuit Movements Using Factorial Analysis

OCCUPATIONAL APPLICATIONS Biomechanical risk factors associated with spacesuit manual material handling tasks were evaluated using the singular value decomposition (SVD) technique. SVD analysis decomposed each lifting tasks into primitive motion patterns called eigenposture progression (EP) that contributed to the overall task. Biomechanical metrics, such as total joint displacement, were calculated for each EP. The first EP (a simultaneous knee, hip, and waist movement) had greater biomechanical demands than other EPs. Thus, tasks such as lifting from the floor were identified as “riskier” by having a greater composition of the first EP. The results of this work can be used to improve a task as well as spacesuit design by minimizing riskier movement patterns as shown in this case study. This methodology can be applied in civilian occupational settings to analyze open-ended tasks (e.g., complex product assembly and construction) for ergonomics assessments. Using this method, worker task strategies can be evaluated quantitatively, compared, and redesigned when necessary. TECHNICAL ABSTRACT Background Astronauts will perform manual materials handling tasks during future Lunar and Martian exploration missions. Wearing a spacesuit will change lifting kinematics, which could lead to increased musculoskeletal stresses. Thus, it is important to understand how suited motion patterns affect injury risk. Purpose The objective of this study was to use the singular value decomposition (SVD) technique to assess movement differences between lifting techniques in a spacesuit with respect to biomechanical risk factors. Methods Joint angles were derived from motion capture data of lifting tasks performed in the MK-III spacesuit. SVD was performed on the joint angles, extracting the common patterns (“eigenposture progressions”) across each task and their weightings as a function of time. Biomechanical risk factors such as total joint displacement, moments at the low back waist joint, and stability metrics were calculated for each eigenposture progression (EP). These metrics were related back to each task and compared. Results The resulting EPs represented characteristic motions that composed each task. For example, the first eigenposture progression (EP1) was identified as waist, hip, and knee motions and the second eigenposture progression (EP2) was described as arm motions. EPs were coupled with different levels of biomechanical stresses, such that EP1 resulted in the greatest amount of joint displacement and low back moment compared to the other EPs. Tasks such as lifting from the floor were identified as “riskier” due to a higher composition of EP1. Differences in EP weightings were also observed across subjects with varying levels of suited experience. Conclusions The linear factorial analysis, combined with biomechanical stress variables, demonstrated an easy and consistent approach to assess injury risk by relating risk to derived EPs and motions. As shown in the lifting analysis and case study example, suited movement strategies or interventions that minimize “riskier” EPs and reduce injury risk were identified. With further development, a future analysis of relevant suited actions can inform mission and suit design.