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.