Development and Validation of an Active Muscle Simplified Finite Element Human Body Model in a Standing Posture.

Active muscles play an important role in postural stabilization, and muscle-induced joint stiffening can alter the kinematic response of the human body, particularly that of the lower extremities, under dynamic loading conditions. There are few full-body human body finite element models with active muscles in a standing posture. Thus, the objective of this study was to develop and validate the M50-PS+Active model, an average-male simplified human body model in a standing posture with active musculature. The M50-PS+Active model was developed by incorporating 116 skeletal muscles, as one-dimensional beam elements with a Hill-type material model and closed-loop Proportional Integral Derivative (PID) controller muscle activation strategy, into the Global Human Body Models Consortium (GHBMC) simplified pedestrian model M50-PS. The M50-PS+Active model was first validated in a gravity standing test, showing the effectiveness of the active muscles in maintaining a standing posture under gravitational loading. The knee kinematics of the model were compared against volunteer kinematics in unsuited and suited step-down tests from NASA's active response gravity offload system (ARGOS) laboratory. The M50-PS+Active model showed good biofidelity with volunteer kinematics with an overall CORA score of 0.80, as compared to 0.64 (fair) in the passive M50-PS model. The M50-PS+Active model will serve as a useful tool to study the biomechanics of the human body in vehicle-pedestrian accidents, public transportation braking, and space missions piloted in a standing posture.

Spacesuit Center of Gravity Assessments for Partial Gravity EVA Simulation in an Underwater Environment.

OBJECTIVE: The objective is to analytically determine the expected CG and build hardware to measure and verify the suited subject's CG for lunar extravehicular activity (EVA) training in an underwater environment. BACKGROUND: For lunar EVAs, it is necessary for astronauts to train with a spacesuit in a simulated partial gravity environment. NASA's Neutral Buoyancy Laboratory (NBL) can provide these conditions by producing negative buoyancy for a submerged suited subject. However, it is critical that the center of gravity (CG) for the human-spacesuit system to be accurate for conditions expected during planetary EVAs. METHODS: An underwater force-transducer system and individualized human-spacesuit model was created to provide real-time measurement of CG, including recommendations for weight placement locations and quantity of weight needed on the spacesuit to achieve a realistic lunar spacesuit CG. This method was tested with four suited subjects. RESULTS: Across tested weighout configurations, it was observed that an aft and high CG location will have large postural differences when compared to low and fore CG locations, highlighting the importance of having a proper CG. The system had an accuracy of ±5lbs of the total lunar weight and within ± 15 cm for fore-aft and left-right CG directions of the model predictions. CONCLUSION: The developed method offers analytical verification of the suited subject's CG and improves simulation quality of lunar EVAs. Future suit design can also benefit by recommending hardware changes to create ideal CG locations that improve balance and mobility. APPLICATION: The developed methodology can be used to verify a proper CG location in future planetary EVA simulations such as different reduced gravity training analogs (e.g. active cable offloading systems).

Evaluating Lumbar Shape Deformation With Fabric Strain Sensors.

OBJECTIVE: To better study human motion inside the space suit and suit-related contact, a multifactor statistical model was developed to predict torso body shape changes and lumbar motion during suited movement by using fabric strain sensors that are placed on the body. BACKGROUND: Physical interactions within pressurized space suits can pose an injury risk for astronauts during extravehicular activity (EVA). In particular, poor suit fit can result in an injury due to reduced performance capabilities and excessive body contact within the suit during movement. A wearable solution is needed to measure body motion inside the space suit. METHODS: An array of flexible strain sensors was attached to the body of 12 male study participants. The participants performed specific static lumbar postures while 3D body scans and sensor measurements were collected. A model was created to predict the body shape as a function of sensor signal and the accuracy was evaluated using holdout cross-validation. RESULTS: Predictions from the torso shape model had an average root mean square error (RMSE) of 2.02 cm. Subtle soft tissue deformations such as skin folding and bulges were accurately replicated in the shape prediction. Differences in posture type did not affect the prediction error. CONCLUSION: This method provides a useful tool for suited testing and the information gained will drive the development of injury countermeasures and improve suit fit assessments. APPLICATION: In addition to space suit design applications, this technique can provide a lightweight and wearable system to perform ergonomic evaluations in field assessments.

Torso Kinematics in Human Rolling Do Not Change When Upper Extremity Motion Is Constrained.

Human rolling, as turning in bed, is a fundamental activity of daily living. A quantitative analysis of rolling could help identify the neuromusculoskeletal disorders that prohibit rolling and develop interventions for individuals who cannot roll. This study sought to determine whether crossing the arms over the chest would alter fundamental coordination patterns when rolling. Kinematic data were collected from 24 subjects as they rolled with and without their arms crossed over their chest. Crossing the arms decreased the mean peak angular velocities of the shoulders (p = .001) and pelvis (p = .013) and influenced the mean duration of the roll (p = .057). There were no fundamental differences in shoulder and pelvis coordination when rolling with the arms crossed over the chest, implying that the arms may not have a major role in rolling.

Ergonomic Risk Identification for Spacesuit Movements Using Factorial Analysis.

OCCUPATIONAL APPLICATIONSBiomechanical 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 ABSTRACTBackground 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.

Lumbar posture assessment with fabric strain sensors.

Astronauts are at risk for low back pain and injury during extravehicular activity because of the deconditioning of the lumbar region and biomechanical demands associated with wearing a spacesuit. To understand and mitigate injury risks, it is necessary to study the lumbar kinematics of astronauts inside their spacesuit. To expand on previous efforts, the purpose of this study was to develop and test a generalizable method to assess complex lumbar motion using 10 fabric strain sensors placed on the torso. Anatomical landmark positions and corresponding sensor measurements were collected from 12 male study participants performing 16 static lumbar postures. A multilayer principal component and regression-based model was constructed to estimate lumbar joint angles from the sensor measurements. Good lumbar joint angle estimation was observed (<9° mean error) from flexion and lateral bending joint angles, and lower accuracy (13.7° mean error) was observed from axial rotation joint angles. With continued development, this method can become a useful technique for measuring suited lumbar motion and could potentially be extrapolated to civilian work applications.