Research Papers

Walking in Simulated Martian Gravity: Influence of the Portable Life Support System’s Design on Dynamic Stability

[+] Author and Article Information
Melissa M. Scott-Pandorf1

Laboratory of Integrated Physiology, Health, and Human Performance, University of Houston, Houston, TX 77204mmscottp@gmail.com

Daniel P. O’Connor, Charles S. Layne

Laboratory of Integrated Physiology, Health, and Human Performance, University of Houston, Houston, TX 77204

Krešimir Josić

Department of Mathematics, University of Houston, Houston, TX 77204

Max J. Kurz1

Laboratory of Integrated Physiology, Health, and Human Performance, University of Houston, Houston, TX 77204; Laboratory of Motion Analysis, Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE 68198-5450mkurz@unmc.edu


Corresponding author.

J Biomech Eng 131(9), 091005 (Aug 05, 2009) (10 pages) doi:10.1115/1.3148465 History: Received August 22, 2008; Revised May 01, 2009; Published August 05, 2009

With human exploration of the moon and Mars on the horizon, research considerations for space suit redesign have surfaced. The portable life support system (PLSS) used in conjunction with the space suit during the Apollo missions may have influenced the dynamic balance of the gait pattern. This investigation explored potential issues with the PLSS design that may arise during the Mars exploration. A better understanding of how the location of the PLSS load influences the dynamic stability of the gait pattern may provide insight, such that space missions may have more productive missions with a smaller risk of injury and damaging equipment while falling. We explored the influence the PLSS load position had on the dynamic stability of the walking pattern. While walking, participants wore a device built to simulate possible PLSS load configurations. Floquet and Lyapunov analysis techniques were used to quantify the dynamic stability of the gait pattern. The dynamic stability of the gait pattern was influenced by the position of load. PLSS loads that are placed high and forward on the torso resulted in less dynamically stable walking patterns than loads placed evenly and low on the torso. Furthermore, the kinematic results demonstrated that all joints of the lower extremity may be important for adjusting to different load placements and maintaining dynamic stability. Space scientists and engineers may want to consider PLSS designs that distribute loads evenly and low, and space suit designs that will not limit the sagittal plane range of motion at the lower extremity joints.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 1

Eigenvalues signify the rate of recovery from a perturbation over multiple strides. The smaller eigenvalue (0.24) takes approximately five strides to recover while the larger eigenvalue (0.54) takes approximately ten strides to recover.

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Figure 2

(a) No divergence is present in the attractor for a harmonic oscillator and the maximum LyE is 0. (b) The x component of the Lorenz attractor contains more divergence and has a maximum LyE of 1.5.

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Figure 3

Demonstration of how the PLSS rig fits on a participant. Backpack straps and a waist belt secure the rig onto the participant. Weights were applied to the back extrusions of the PLSS rig to create the aft condition here. Retroreflective markers were placed on the participant’s heel, metatarsal-phalange joint, lateral malleolous, lateral epicondyle, greater trochanter, and acromion-clavicle joint.

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Figure 4

PLSS rig load configurations (forward, aft, high, low, and normal). The configurations were based on potential design applications for a new PLSS device. The arms of PLSS were designed to sit 15–25 cm from the body in order to allow free motion of the individual’s limbs.

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Figure 5

(a) sample Poincare section in state space transecting the trajectory of the joint pattern. The eigenvalues in FA quantify if the distances between the equilibrium point and each individual step position (e.g., x(n) and x(n+1) grow or decay. (b) sample Poincaré map (i.e., plot of a step (X(n)) to a subsequent step (X(n)+1) of the knee at midswing. The equilibrium point (x∗) for the sample Poincaré map shows that the average knee angular position at midswing was 42.9 deg.

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Figure 6

An example of a knee attractor during locomotion. The euclidian distance between two neighboring points is calculated at two points in time (e.g., S(0) and S(i)) to determine the divergence in the system.

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Figure 7

(a) the time lag is determined to be at the first local minimum of the curve, which in this case is at 34 frames. (b) the embedding dimension is determined when the global false nearest neighbors curves drop to zero, indicating no false neighbors are present in the data. Here the embedding dimension would be 4.

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Figure 8

The linear region of the divergence curve was used to calculate the slope. The slope of the curve indicates the average rate of local divergence in the reconstructed attractor.

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Figure 9

β values for stance and swing during the different load locations. * denotes significantly different from normal, ◼ denotes significantly different from low, and ● denotes significantly different from aft.

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Figure 10

The equilibrium points (i.e., average joint positions ±SD) during midstance for the ankle, knee, and hip in each load placement condition. * denotes significantly different from normal, low, and forward, while ◼ denotes significantly different from all other conditions.

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Figure 11

The equilibrium points (i.e., average joint positions ±SD) during midswing for the ankle, knee and hip in each load placement condition. * denotes significantly different from low and ◼ denotes significant different from aft.

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Figure 12

The number of participants that walked with the ankle dorsiflexed (forefoot strike) and plantarflexed at midstance

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Figure 13

Maximum LyE values for the five load placement conditions. No significant differences were found between conditions (p>0.05).




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