Postural Strategies Used While Donning a Simulated xEMU Spacesuit

Abstract

Our goal is to further understand how a simulated extravehicular mobility unit (xEMU) spacesuit affects the relative movement of one’s body segments. The effect of the xEMU spacesuit on gait is not yet fully understood. Here, gait was examined in terms of postural strategies, defined by the absolute angle with standard deviation (AAD) and the anchoring index (AI). The AAD values allowed the measurement of the absolute angles of body segments and their standard deviation, whereas the AI provided a measure of how stable a body segment was relative to a global reference frame and the inferior body segment. The body segments examined were the head, thorax, lumbar, and pelvis segments of 17 participants (26.53 ± 6.51 years old). The configurations tested included unsuited, or using a xEMU Vest or a hard upper body torso (HUT) for four walking conditions: eyes open/closed, with either forward or backward walking. The AAD values of the xEMU Vest were insignificant compared to those of the unsuited condition. The HUT significantly affected the AAD values compared to the unsuited condition. The AI for the HUT also indicates a new unique postural strategy being employed by the HUT group that was not previously observed.

1. Introduction

Although critical for human space travel, it is not fully understood how spacesuits affect an astronaut’s ability to move. In a general sense, an astronaut’s range of motion is restricted, and their center of gravity (COG) is superiorly shifted and moved away from the body when donning a spacesuit and portable life support system (PLSS) [1]. This is evident in the difficulties astronauts experienced once on the moon; as observed from video footage, traversing the moon appeared to be very difficult [2]. The types of postural strategies used by astronauts while on lunar and Martian surfaces have not been previously investigated. Astronauts in future missions will have to perform various activities on the surfaces of the Moon or Mars [3] (e.g., manually drilling/digging to acquire soil samples or unloading supplies and equipment) that will require maintaining stable postures. NASA has an interest in examining gaps that relate to investigating postural control strategies [4]. Developing an understanding of how body segments move during gait, and relative to each other, when donning a spacesuit is beneficial for future generations of spacesuits and astronaut mobility. In particular, here we were interested in how the COG shift up and away from the body, due to the spacesuit, affects postural control.

Human locomotion, or gait, can be described as an activity that induces corresponding rhythmic oscillations of the trunk and head [5,6]. These movements are interpreted as equilibrium strategies, or postural strategies, aimed to provide stabilization while standing or moving (e.g., walking, running, or performing tasks). The various balance strategies adopted by children and adults involve two main functional principles: (1) the choice of the reference frame on which the equilibrium control is based and (2) the degrees of freedom of the various body joints. For the first principle, the reference frame can be either the supporting surface on which the subject is standing or the direction of gravity (e.g., earth-vertical) [7]. When the reference frame is the supporting surface, balance control is temporally organized from the feet to the head, according to an ascending organization. However, when the reference frame is the gravitational vector, balance control is temporally organized from the head to the feet, according to a descending organization [8]. These are known as bottom-up and top-bottom recruitment [7,8], which are two types of postural strategies. The second functional principle concerns the choice of the degrees of freedom of the various body joints, which must be controlled simultaneously in dynamic equilibrium according to the task’s constraints and/or the subject’s motor ability. Postural control during stance consists of superimposed modules, which can be controlled independently [8]. Trunk stabilization can be defined as maintaining control over trunk posture and movement, despite the disturbing effects of gravity and external and internal perturbations [9]. Trunk stabilization is dependent on the passive (osteoligamentous), active (muscular), and neural sub-systems that contribute mechanically and in terms of acquiring and processing information to guide mechanical responses [10]. Head and trunk stabilization in space have been shown to be critical in determining stability [5,6,7]. The head in normal walking conditions maintains stability during gait, and there is an established relationship between the head and trunk during gait [6,7]. Using head and trunk stabilization, one study [5] was able to characterize human gait using the absolute angular dispersion and anchoring index (AI) for forward and backward walking. However, in this proposed study, we used the absolute angle with standard deviation.

The purpose of our study was to generate new knowledge of how spacesuits may affect how postural control during gait. Postural strategies are a combination of muscle synergies, the activation of groups of muscles to achieve the desired end goal [11], and posture control during static or dynamic movement. Here, we quantified postural strategies by the absolute angle with standard deviation (AAD) between four identified body segments and an anchoring index (AI) for each body segment. The AI has previously been used to characterize how a body segment(s) is balancing relative to the inferior (or anatomically lower) body segment, and to a global reference frame [5]. Since older adults have a restricted range of motion compared to younger healthy adults, it was hypothesized that the postural strategies implemented by the participants with a COG shift would resemble the AI of older adults. Our study provides important insight into how astronauts’ donning of an extravehicular mobility unit (or xEMU) spacesuit may affect their postural control.

2. Materials and Methods

Research experiments were conducted at the Center of Biomechanical & Rehabilitation Engineering (CBRE) at the University of the District of Columbia (UDC), in Washington, DC, and at the Anthropometry & Biomechanics Facility (ABF) at the NASA Johnson Space Center (JSC) in Houston, TX. Within the UDC CBRE, the experimental protocol was approved by the Institutional Review Board (Protocol ID: 1837316-1) and informed consent was given by the participants prior to engaging in the study. An overview of the research study is shown in Figure 1.

Participants were recruited via flyer postings around the University campus, as well as by word of mouth. Seventeen physically fit and healthy individuals (6 males and 11 females) aged 20–40 years old (26.53 ± 6.51 years old), participated in the study. None of the participants had a previous injury or condition, nor were they taking any medications that affected their balance and/or gait. No prior training was required for the experiments. Two participants had prior experience in a spacesuit.

The xEMU spacesuit raises the COG superiorly on the abdomen, while the portable life support system (PLSS), which is roughly the same weight as the xEMU spacesuit [1], further raises the COG and shifts it away from the body. To induce an analogous COG shift to the xEMU spacesuit [1], a weighted vest (or xEMU vest) or a hard upper body torso (or HUT) were used (Figure 2A,B). The xEMU Vest concentrates the weight on the torso and shifts the COG superiorly to mimic the superior shift of the COG observed with the xEMU spacesuit. The xEMU Vest weighed 20 lbs (9.072 kg), concentrated at the torso. The HUT is a rigid, 3D-printed torso section of the xEMU spacesuit that weighed approximately 11 lbs (4.98 kg). The HUT simulated the spacesuit’s shift in COG, both superior to and away from the body and restricted the range of motion in the upper limbs and neck. The HUT altered the resting standing posture of the individual, shifting the shoulders forward, causing the participant to assume a more natural “astronaut-like” stance (Figure 2C); the wearer leans forward, and the arms and shoulders appear to be hunched over. Participants were tested unsuited (control) and while donning the xEMU Vest (Figure 2A), as well as using the HUT (Figure 2B).

2.1. Experimental Protocol

A Vicon Nexus Motion Capture system (Vicon Motion Systems Ltd., Oxford, UK, software 12.4) was used to capture the data at both UDC and NASA. At UDC CBRE laboratory, 12 bonita10 cameras were used to record the walking trials while at NASA JSC ABF laboratory, 11 MX-F20 cameras were used. Both camera systems were supplied by Vicon Nexus and can be seen in Figure 3. Participants were prepped by laboratory assistants and supervised by the laboratory director, and then stepped into the recording volume. Trial 1 consisted of forward-walking the length of the recording volume back and forth (i.e., the participants started from one edge of the recording volume (the starting point) and walked towards the opposite edge of the recording volume; once at the opposite edge, the participants would turn 180 degrees and walk back to the starting point); this was repeated twice. The forward-walking trials gave a total of four forward-walking cycles in one trial. Trial 2 consisted of backward walking. Participants were instructed to listen to the walking cues given by the laboratory assistants to begin and stop their backward-walking trials as they neared the edge of the recording volume. The participants would then stop and turn 180 degrees and proceed to backward walk to the starting point, repeating twice. For both forward- and backward-walking trials, there were four walking cycles each per trial. The CBRE laboratory and ABF laboratory had two testing configurations: unsuited and xEMU Vest. Two trials each were performed unsuited and when donning the xEMU Vest. The third configuration, the HUT, was implemented for testing only onsite at NASA.

To determine body movements tied to four body segments (the head, thoracic, lumbar, and pelvic segments), eight reflective motion capture markers were affixed with double-stick tape to each participant. In Figure 3B, markers shown in blue are the left and right back of the head markers (LBHD and RBHD), C7 marker, T10 marker, both left and right posterior superior iliac spine markers (LPSI and RPSI), and both left and right thigh markers (LTHI and RTHI). Markers shown in black are virtual markers for the center of the back of the head (CBHD), center posterior superior iliac (CPSI), and center thigh (CTHI), which were created in software to help identify the body segments.

2.2. Data Analysis

Prior to analyzing the participants’ data, post-processing had to be completed for all the motion capture files. Post-processing included labeling markers and filling in any gaps present in the trials. This was performed using Vicon Nexus (software 12.4). Once all the markers had been labeled and gaps filled in, the marker data (x, y, and z positioning) for the markers shown in Figure 3B were exported into a .txt file to be analyzed in MATLAB. MATLAB R2022B (MathWorks, Inc., Natick, MA, USA, R2022b) was used to analyze the position data of the markers.

Absolute angle with standard deviation, or AAD, is based on the absolute position of markers/indicators that are used to define a specific body segment. In this paper, we investigated four body segments: the head, thorax, lumbar, and pelvis segments. Axes were as defined in [6], the segmental angular movement (rotation) about the anterior–posterior axis (x-axis) is defined as the pitch angle, the rotation about the transverse axis (y-axis) is defined as the roll angle, and the rotation about the vertical axis (z-axis) is defined as the yaw angle. The global axis can be seen in Figure 3A; please note that our definition of AAD is based on the absolute angle, and so it is different from the definition by [6]. All three angles are calculated by using the inverse trigonometric function of the tangent function, the arctan. The normalized AI compares the stabilization of a given segment with respect to both the external space and the underlying anatomical segment [5]. For example, the head segment AI can be found by using the laboratory space reference (external space/global reference) and the thorax segment, which is located directly under the head segment.

The calculation of the AAD was undertaken according to the laboratory or global x, y, and z axes, with the participants walking along the y-axis. The positive y-axis pointed forward, the positive x-axis pointed to the participant’s right, and the positive z-axis pointed upward. The AAD values of each segment are based on the absolute positions of two markers that define the segment, which can be seen in Figure 3B. For each segment, three AAD values are calculated with respect to three rotational axes: pitch, roll, and yaw. The segmental angular movement around the frontal plane (medial lateral/y-axis) is defined as the pitch angle. The movement around the sagittal plane (anterior posterior/x-axis) is defined as the roll angle. The movement around the transverse plane (longitudinal/z-axis) is defined as the yaw angle. Equation (1) shows how to calculate the absolute pitch angle, ${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{P}}$, for the head segment using the markers CBHD and C7. CBHD is the center of the back of the head (a virtual marker) and C7 is vertebra C7. All three angles are calculated by using the inverse trigonometric function of the tangent function arctan. Equation (2) shows the absolute roll angle of the head segment, ${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{R}}$, and Equation (3) shows the absolute yaw angle, ${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{Y}}$, for the head segment. The markers used change, depending on the segment.

$${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{P}}={\tan }^{-1}\left({\displaystyle \frac{{\mathrm{X}}_{\mathrm{C}\mathrm{B}\mathrm{H}\mathrm{D}}-{\mathrm{X}}_{\mathrm{C}7}}{{\mathrm{Z}}_{\mathrm{C}\mathrm{B}\mathrm{H}\mathrm{D}}-{\mathrm{Z}}_{\mathrm{C}7}}}\right)$$

(1)

$${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{R}}={\tan }^{-1}\left({\displaystyle \frac{{\mathrm{Y}}_{\mathrm{C}\mathrm{B}\mathrm{H}\mathrm{D}}-{\mathrm{Y}}_{\mathrm{C}7}}{{\mathrm{Z}}_{\mathrm{C}\mathrm{B}\mathrm{H}\mathrm{D}}-{\mathrm{Z}}_{\mathrm{C}7}}}\right)$$

(2)

$${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{Y}}={\tan }^{-1}\left({\displaystyle \frac{{\mathrm{X}}_{\mathrm{C}\mathrm{B}\mathrm{H}\mathrm{D}}-{\mathrm{X}}_{\mathrm{C}7}}{{\mathrm{Y}}_{\mathrm{C}\mathrm{B}\mathrm{H}\mathrm{D}}-{\mathrm{Y}}_{\mathrm{C}7}}}\right)$$

(3)

From the AAD values, the relative angle can be calculated. The relative angle for the head segment in the roll direction, ${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{r}}}^{\mathrm{R}}$, is equal to the absolute angle of the head in the roll direction, ${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{R}}$, minus the absolute angle of the thorax in the roll direction, ${\mathsf{\theta }}_{{\mathrm{T}}_{\mathrm{a}}}^{\mathrm{R}}$.

$${\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{r}}}^{\mathrm{R}}={\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{R}}-{\mathsf{\theta }}_{{\mathrm{T}}_{\mathrm{a}}}^{\mathrm{R}}$$

(4)

Using the relative angle (Equation (4)) and the absolute angle (Equation (2)), the AI is calculated (Equation (5)). AI indicates how stable a specific anatomical segment of the human body is. AI ranges from +1 to −1, with +1 indicating stability relative to space and −1 indicating stability relative to the inferior body segment. As an example, the AI for the head segment, AI(H), is shown in Equation (5). To find the AI of the head segment, the variance of the relative angle and absolute angle must be calculated. The relative angle is subtracted from the absolute angle, while multiplying the variance, ${\mathsf{\sigma }}^2$, for each variable respectively (Equation (5), numerator). Next, divide by the sum of the relative angle and the absolute angle, multiplying each variable with their variance, ${\mathsf{\sigma }}^2$, respectively (Equation (5), denominator).

$$\mathrm{A}\mathrm{I}\left(\mathrm{H}\right)={\displaystyle \frac{{\mathsf{\sigma }}^2\left({\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{r}}}^{\mathrm{R}}\right)-{\mathsf{\sigma }}^2\left({\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{R}}\right)}{{\mathsf{\sigma }}^2\left({\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{r}}}^{\mathrm{R}}\right)+{\mathsf{\sigma }}^2\left({\mathsf{\theta }}_{{\mathrm{H}}_{\mathrm{a}}}^{\mathrm{R}}\right)}}$$

(5)

The thorax and lumbar segments AI are calculated in the same matter. The AAD values for each axis (roll, pitch, and yaw) were calculated using the appropriate markers for the selected body segment (see Figure 3B for markers) using Equations (1)–(3). Subsequently, the relative angles were calculated using Equation (4). With both the relative angles and AAD values, the AI was then calculated using Equation (5) for the selected body segment. The AAD values and AI were calculated for each segment, except for the pelvic segment. The pelvic segment was demonstrated to be significantly stable [5] during multiple test conditions: eyes opened/closed over hard ground and eyes opened/closed over foam mats.

2.3. Statistical Analysis

For statistical analysis, the software RStudio version 4.2.3 was used. The data were non-normally distributed. Therefore, the Wilcoxon rank-sum significant test method was used to test specific groups’ significance (between the different test configurations) while the Kruskal–Wallis’, a one-way ANOVA significance test was also used to test overall group significance. The Holm–Bonferroni method was used to cross-check any significant findings to prevent type 1 errors.

3. Results

The AAD values and AI show how the participants collectively performed in each walking condition (forward or backward walking) and in each configuration (unsuited, xEMU Vest, or HUT). For the AAD values in the forward-walking condition, the participants had relatively zero movements on the transverse axis (x-axis), and almost zero AAD in the roll direction for both unsuited and xEMU Vest conditions, as shown in Figure 4A. This indicates that when unsuited or donning the xEMU Vest, the participants were stable and maintained their head centered when in gait, and that the participants did not demonstrate significant sway (side-to-side movement) for either walking condition (forward and backward walking). The AAD values changed with the HUT. The HUT altered the resting posture of the participants, as shown in Figure 2C, and therefore could have been the reason behind the negative (a slight lean to the left) AAD for the head in the roll direction. For the pitch in the forward-walking condition, there were positive AAD values on the anterior–posterior axis (y-axis), which indicated the participants were looking upwards and straight ahead in the unsuited and xEMU vest conditions. However, the HUT had the thorax and pelvis as negative (these body segments were facing downward). The AAD values for the HUT data differed; the HUT altered the resting posture of the participants to a more hunched-over posture, thereby limiting the amount of pitch angle to successfully face upward and forward while walking. The HUT also differed during the backward-walking conditions for the pitch: the AAD values for the head, thorax, and lumbar segments were all negative, and suggest that the participants were leaning forward and facing downward during backward walking.

This can be seen as the participants walking backward without correcting their altered posture due to the HUT. The participants may have found walking backward while looking at the ground more stable or comfortable than correcting their posture to face forward and with the head raised. In the yaw direction, there was a mixture of both positive and negative AAD values on the vertical axis (z-axis), as shown in Figure 4. This indicates some body segments were twisting a few degrees either to the left (negative) or to the right (positive) during forward walking. The xEMU Vest kept all the body-segment AAD values positive in both the forward and backward walking conditions, while in the unsuited condition, the head and lumbar segments were both moving together in the negative direction in both walking conditions. The HUT again differed from the other two configurations and favored movement to the left, once again. The HUT data had a trend of favoring movement to the left in both roll and yaw directions. It is unclear why the participants favored their left side when donning the HUT, but the trend was certainly noticeable in forward and backward walking. While donning the HUT, the head in the roll direction favors the left (negative) movement in forward and backward walking, while in the yaw direction the head, thorax, and pelvis favor twisting to the left in forward walking and the head and pelvis favor the left in backward walking.

From the AAD values, there were a total of 18 significant findings. The altered resting posture with the HUT may have impacted how the participants moved during gait in that the HUT configuration accounted for 17 out of the 18 significant parameters found in the AAD values.

Table 1. Pitch and yaw AAD parameter values across test conditions (forward and backward walking) for test configuration comparisons (unsuited vs. xEMU Vest, unsuited vs. HUT, and xEMU Vest vs. HUT). The top value is for the first listed configuration, bottom value is the second listed configuration. Color-coded to represent the degree of significance.

Config. Comparison	Head Pitch	Thorax Pitch	Lumbar Pitch	Pelvis Pitch	Head Yaw	Thorax Yaw	Lumbar Yaw	Pelvis Yaw
Forward Walking
Unsuited xEMU	2.32	2.15	1.59	1.561	−3.449	4.573	−6.23	3.22
	2.253	1.991	1.682	1.464	1.013	3.637	4.307	2.481
Unsuited HUT	2.32	2.15	1.59	1.561	−3.449	4.573	−6.23	3.22
	2.844	−0.656	−0.004	−0.694	−10.593	−5.93	2.76	−1.004
xEMU HUT	2.253	1.991	1.682	1.464	1.013	3.637	4.307	2.481
	2.844	−0.656	−0.004	−0.694	−10.593	−5.93	2.76	−1.004
Backward Walking
Unsuited xEMU	2.267	1.877	1.432	1.37	0.884	2.871	−7.115	3.192
	2.054	2.052	1.401	1.039	2.375	3.167	4.678	3.328
Unsuited HUT	2.267	1.877	1.432	1.37	0.884	2.871	−7.115	3.192
	−13.6	−1.754	−0.214	−0.268	−3.551	11.209	12.656	−0.888
xEMU HUT	2.267	2.052	1.401	1.039	2.375	3.167	4.678	3.328
	−13.6	−1.754	−0.214	−0.268	−3.551	11.209	12.656	−0.888

Significance: p = 0.01 < 0.05, p = 0.001 < 0.01, p = 0.0001 < 0.001.

displays the average AAD values for each parameter, excluding the roll parameters, as none were significant, in the forward- and backward-walking conditions, and it is color-coded to indicate the level of significance.

The AIs for the head, thorax, and lumbar (the pelvis AI was not calculated, since no data were analyzed from the anatomical body segment below the pelvis) segments in the forward- and backward-walking conditions showed a substantial number of significant findings. From the forward-walking condition alone, there were 16 significant findings. In the backward-walking condition, there were another 12 significant findings, for a total of 28 significant findings in the AIs.

Table 2. All AI parameter values across test conditions (forward and backward walking) and for test configuration comparisons (unsuited vs. xEMU Vest, unsuited vs. HUT, and xEMU Vest vs. HUT). The top value is for the first listed configuration, bottom value is the second listed configuration. Color-coded to represent the degree of significance.

Config. Comparison	Head Roll	Thorax Roll	Lumbar Roll	Head Pitch	Thorax Pitch	Lumbar Pitch	Head Yaw	Thorax Yaw	Lumbar Yaw
Forward Walking
Unsuited xEMU	−0.147	−0.536	0.199	−0.306	0.840	0.292	−0.023	0.938	0.007
	−0.152	−0.747	0.0866	−0.535	0.399	0.572	−0.538	0.688	−0.005
Unsuited HUT	−0.147	−0.536	0.199	−0.306	0.840	0.292	−0.023	0.938	0.007
	−0.689	0.109	0.489	0.14	0.57	0.425	0.595	0.645	−0.115
xEMU HUT	−0.152	−0.747	0.087	−0.535	0.399	0.572	−0.538	0.688	−0.005
	−0.689	0.109	0.489	0.14	0.57	0.425	0.595	0.645	−0.115
Backward Walking
Unsuited xEMU	−0.346	−0.649	0.346	−0.299	0.636	0.407	0.107	0.995	0.025
	−0.255	−0.783	0.197	−0.566	0.313	0.527	−0.464	0.900	−0.037
Unsuited HUT	−0.346	−0.649	0.346	−0.299	0.636	0.407	0.107	0.995	0.025
	−0.573	−0.303	0.462	−0.033	0.523	0.212	0.552	0.56	−0156
xEMU HUT	−0.255	−0.783	0.197	−0.566	0.313	0.527	−0.464	0.900	−0.037
	−0.573	−0.303	0.462	−0.033	0.523	0.212	0.552	0.56	−0156

Significance: p = 0.01 < 0.05, p = 0.001 < 0.01, p = 0.0001 < 0.001.

shows all the AIs for each parameter. The table is color-coded for significance.

Interestingly, the significant parameters for forward walking were almost mirrored perfectly backward walking. The forward-walking pitch direction was the most affected, with almost all the pitch parameters experiencing significant differences, followed closely by the yaw. Figure 5 represents the AIs in plot form. The forward-walking AIs for the pitch and yaw directions almost mirrored their counterparts in the backward-walking condition. However, the xEMU Vest and HUT behaved differently. The xEMU Vest significantly decreased the AI of the head in both walking conditions for the pitch and yaw, while the HUT significantly increased the AI of the head with the same parameters. This was then reversed for the thorax in the roll direction. The xEMU Vest further deepened the dependability of the thorax on the inferior (lower) anatomical body segment, while the HUT pushed the thorax to become more independently stable in both forward- and backward-walking conditions.

However, there were some similarities as well. The xEMU Vest and HUT AIs fell close together and trended in the same direction for the thorax and lumbar segments in the pitch and yaw directions for both walking conditions, respectively. This could be an indication of similar results; however, different postural strategies were implemented to achieve the desired balance during gait. The HUT causes the head to become more independently stable in both walking conditions for the pitch and yaw directions, while in the roll direction, it is dependent on the thorax. This is the exact opposite of the xEMU Vest. The xEMU Vest causes the head to become dependent on the thorax in the pitch and yaw directions while not significantly affecting the head balance in the roll direction. The xEMU Vest and HUT cause different behaviors.

4. Discussion

Here, we were able to determine how COG shifts, due to the xEMU Vest and HUT, affect postural strategies during gait. According to the AAD results, the xEMU Vest does not significantly affect the following four body segments: head, thorax, lumbar, and pelvis. The xEMU Vest does not significantly influence how the body segments move with each other or in space. The body segments follow the same path of motion regardless of whether the xEMU Vest is donned, but this is not the case for the HUT. The HUT significantly influences how body segments move with each other. The postural strategy employed by the participants may have been similar between unsuited and the xEMU Vest. However, the xEMU Vest does significantly impact how the body segments balance during gait. There were significant differences in both the forward- and the backward-walking trials AIs. The xEMU Vest significantly decreased the independent stability of the head and further deepened the dependability of the thorax on the lower body segment. The head and thorax body segments, in turn, started to rely on the inferior anatomical body segments to balance during gait. Previous studies with healthy adults [5] demonstrated that in healthy adults with eyes opened walking forward on hard ground, the head roll and pitch AI is just slightly above 0, meaning it does not rely on the anatomical body segment below it. We found similar results: walking forward on the hard ground surface while unsuited resulted in head roll and pitch AIs just slightly below 0. We believe this difference to be within the margin of error between the two independent studies. However, with the xEMU Vest, the pitch and yaw AI decreased significantly and made the head reliant on the thorax, the inferior anatomical body segment below it for both the walking conditions. Past studies conducted on adults and children [5,6] suggested that the head will become more independently stable, with a positive AI approaching +1, when the difficulty of equilibrium during gait is increased; this was what was observed for the HUT AIs. If increasing the difficulty in balancing during gait leads to a head AI approaching +1, then a head AI approaching −1 equates to a decrease in difficulty in balancing during gait. The xEMU Vest does not make balancing during gait more difficult, but instead it does the opposite, it makes it easier. The HUT AIs did follow the findings of previous studies in that the head increased in independent balance. However, the AIs only increased in the pitch and yaw directions, while the AI decreased in the roll. The decrease in AI, or the increased dependency on the lower body segment in the roll direction, is an indication that a different postural strategy is deployed when donning the HUT.

The AAD values indicate that the outcome of the implemented postural strategy was the same with/without the xEMU Vest, but the AIs show that the postural strategies chosen by the 17 participants were indeed different. Humans are conditioned to walk in a certain way: Assaiante and Amblard [6] concluded that children aged seven and above started to implement the head stabilization strategy in space. The head is stabilized independently from the body during gait. The xEMU Vest changes this and makes the head reliant on the thorax (inferior anatomical body segment) to balance in space, which is similar to what occurs in children aged 6 and under. With the participants donning the xEMU Vest, their COG shifted superior and created a “top-heavy” balance point. Children who are 6 years old and under also have a “top-heavy” balance point, or superiorly shifted COG, since children’s bodies need to grow to be able to support their heads. This could be the reason why the participants began to employ a bottom-up postural strategy, since the xEMU Vest shifted their COG sufficiently high to cause similar balance conditions to when the participants were young children, which is considered to be a simpler postural strategy because it minimizes the degrees of freedom needed to exert control simultaneously [6].

The unsuited data created a baseline of postural strategies used to balance during gait. The xEMU Vest changed the postural strategies slightly. This change was not detectable with only the AAD values, but it was detectable with the AIs. Instead of postural strategies similar to those of older adults, the xEMU Vest coincides with postural strategies employed by young children, while the HUT changed the postural strategies employed and was detectable with the AAD values and AIs. The HUT postural strategy employed may be a separate postural strategy specifically for the HUT and, possibly, for an actual xEMU spacesuit.

Studies conducted in microgravity have touched on balance, but only briefly. Some studies focused on ankle, knee, hip, and trunk moments [14] during bending at the waist and were able to conclude that different ankle strategies were imposed during microgravity from when in normal Earth gravity. A difference in ankle strategy may be an indication of different postural strategies. AAD values and AIs may show indications of different postural strategies used in microgravity conditions. A study conducted in 2010 [15] showed that the angular dispersions and AIs of a forearm are significantly affected in microgravity compared to normal Earth gravity. However, this study was performed with sit-to-stand tasks and not gait tasks. As for postural strategies employed in fully-fledged and functional xEMU spacesuits, the characteristics of the HUT may be shared and even further deepened. This means that the postural strategies may be even more unique than the HUT postural strategies shown here and that they require a more extensive redesign of the spacesuit to mimic unsuited postural strategies if the goal for future spacesuit designs is to mimic unsuited gait as much as possible.

For future work, working with a fully functional xEMU spacesuit (or the latest generation of spacesuit available) would be ideal. It is important to note that this is among the first gait characterization studies conducted involving spacesuits. The few previous gait studies focused on gait mobility, including the range of motion of the hip-brief assembly, the range of motion of the shoulders, and the mobility of the gloves/hands [12]. More studies are required to fully understand the postural strategies employed when donning a spacesuit. Other factors are also worth investigating. With a larger participant pool, other characteristics, such as height, weight, and age, can be analyzed with deep learning techniques to determine any correlations with postural strategies and gait stability while donning a spacesuit.