Mechanical performance evaluation of science museum seating systems using FEM

Abstract

This paper explores the structural design and material application of seating systems in science museums, focusing on safety and ergonomic comfort. To address the requirements of modern exhibition environments, the study employs finite element analysis (FEA) and body pressure distribution experiments to evaluate the mechanical performance and user comfort of four distinct seat structures and materials: polyurethane, polyester fiber, polypropylene foam, and memory foam. The investigation analyzes their effects on stress distribution, strain, and pressure relief. Results indicate that while structural design is critical for ensuring uniform stress distribution, material properties primarily dictate comfort and pressure alleviation. Specifically, memory foam and polyurethane demonstrate the optimal balance between comfort and load-bearing capacity, whereas polypropylene presents risks of localized stress concentration. Consequently, the study proposes a “structural optimization coupled with material matching” strategy, utilizing rigid materials for load-bearing and flexible materials for comfort interface. These findings provide practical guidance for the engineering design and material selection of public seating in science museums.

Introduction

The development of science museums is essentially a historical narrative of continuous innovation in paradigms of scientific communication, clearly demonstrating the evolutionary path from “static display” to “dynamic interaction¹.” This evolution not only reflects advancements in technology across eras but also embodies the iteration of science education philosophies. Early science popularization venues mostly adopted a display-case model, with “do not touch” as a common rule. Exhibits were primarily static models, resulting in a one-way dissemination of knowledge. While such exhibitions carried authority, they lacked public engagement mechanisms, making it difficult for visitors to immerse themselves in the authentic cognitive process of scientific exploration².

The “interactive experience revolution” led by the San Francisco Exploratorium broke this impasse. The museum introduced the core exhibition concept of “hands-on practice³,” enabling visitors to gain an intuitive understanding of scientific knowledge such as physical principles through interactive exhibits like lever mechanisms. This transformation not only broke through traditional exhibition formats but also marked a shift in science communication from “passive indoctrination” to “active participation,” and from “abstract cognition” to “concrete experience,” representing a qualitative change in science education philosophy⁴.

With the widespread application of digital technology, science museums have entered an “immersive experience” phase. Technologies such as virtual reality (VR), augmented reality (AR), and motion sensing enable multi-dimensional interactive scenarios⁵, allowing visitors to engage with exhibits through full-body, multi-sensory interaction. These advances strengthen engagement and meaning construction in science communication⁶.

This study focuses on the mechanical design and material selection of public seating in science museums^12–15,23. It employs finite element analysis (FEA)^21–25 and pressure-mapping experiments⁹ to evaluate static support performance^7,8 Fig. 1.

Fig. 1.

Schematic illustration of research background and motivation.

In terms of responsive design, the interactive seat serves as the core interaction node, establishing an intelligent connection channel between visitors and exhibition content. Users can access in-depth information and customized explanations about target exhibits in real time via a voice interaction interface. The system can also generate personalized visit path recommendations based on users’ personal profiles and behavioral data. When the system detects that a user has entered a resting state, the seat pushes related knowledge graphs and activity previews through the edge computing unit, achieving seamless integration between the resting process and knowledge acquisition¹⁰. The response system adopts a distributed processing architecture to ensure that command processing latency is controlled at the millisecond level, effectively enhancing the overall visiting experience¹¹.

Materials and methods

Finite element analysis experimental equipment and objects

The experiment will select four different types of study chairs to construct finite element simulation models in Fig. 2.

Fig. 2.

Schematic diagram of four different types of seat cushions.

The four cushioning materials considered in this study were polyurethane foam (PU), Dacron, polypropylene foam (PP), and memory foam. To describe their nonlinear compressive behavior in the finite element simulations, the foam-like materials were represented using a third-order Ogden hyperelastic constitutive model, in which µ1–µ3 denote the shear-related material coefficients and α1–α3 denote the strain-hardening exponents. The parameter sets listed in Table 1 were compiled from published constitutive calibration studies for polyurethane foams, expanded polypropylene foams, and memory-foam-like polymeric materials. For Dacron, which is primarily used as a compliant padding or interface layer rather than as a bulk foam support material, an equivalent compressible hyperelastic layer was adopted to represent its pressure-interface behavior in the present simulations. In the revised manuscript, the source of each material parameter set has been clarified and linked more explicitly to the corresponding reference(s), so that the origin of the constitutive inputs used in the simulations can be clearly traced.

Table 1.

Physical properties. Note: The Ogden parameters for PU, PP, and memory-foam-like materials were compiled from published constitutive calibration studies and adapted to the present quasi-static seat-contact simulations. Dacron was modeled as an equivalent compliant hyperelastic layer to represent its interface-support behavior rather than as a bulk foam core material.

Body pressure distribution testing equipment and subjects

The body pressure distribution test experiment utilized the Tactilus 4.0 (Sensor Products LLC in Madison, NJ) body pressure distribution measurement device, with a pressure measurement range of 0–30 psi, accuracy of ± 5%, dimensions of 18.3 × 18.3 inches, and 32 × 32 sensor units. However, raw data from the experiments, including test images and participant feedback, will be provided in supplementary materials for reproducibility.

Thirty healthy participants (15 males and 15 females; age: 22 ± 1.5 years, height: 1.69 ± 0.05 m, weight: 60.1 ± 12.5 kg) were recruited from a university. The study was approved by the review committee, and informed consent was obtained from the participants prior to the experiment. All participants had no history of physical pain or illness in the past month, had not engaged in vigorous exercise prior to the experiment, and wore lightweight cotton clothing without metal or other foreign objects during the experiment. The experiment was conducted in the human factors laboratory of a local university, which had good ventilation and maintained standard temperature and humidity conditions. A single-blind experiment was employed, requiring participants to sit in a standard upright posture on the designated experimental chair. Participants were unaware of the material of the seat being tested, and the testing duration was 5 min. Participants were required to complete a questionnaire using the semantic differential method to assess their subjective perceptions of five evaluation factors: fit, support, buttock comfort, thigh comfort, and calf comfort. Scores were rated on a 7-point scale, with 1 being the worst and 7 being the best.

Because the Tactilus 4.0 system has a nominal measurement range of 0–30 psi and a stated accuracy of ± 5%, the present study treats the pressure-mapping results primarily as a comparative tool for evaluating relative differences among seat-material combinations tested under identical conditions, rather than as a high-precision device for absolute low-pressure quantification. To reduce measurement variability, all participants were tested under the same sitting posture, clothing condition, environmental setting, acquisition procedure, and measurement duration. In addition, the analysis focused not only on absolute pressure magnitude, but also on comparative trends in peak pressure, mean pressure, contact area, and pressure-gradient distribution. Therefore, although device-level uncertainty may affect the exact absolute values recorded in the low-kPa range, the relative differences and distribution patterns reported in this study remain meaningful for comparative assessment of seating performance.

Establishment of seat simulation models

This study used the modeling tools provided in ABAQUS to construct three-dimensional geometric models of the seating systems based on their actual dimensions and structural features. Since the preprocessing stage is critical to both the accuracy and efficiency of the finite element solution, particular attention was paid to geometric modeling, mesh generation, material definition, and the application of constraints and loads. The general principle of model development was to preserve the essential geometric and mechanical characteristics of the seating systems while maintaining reasonable computational efficiency. To implement the simulations, the finite element models were established with the key parameters and specifications listed in Table 2.

Table 2.

Finite element model setup.

Analysis steps	Parameter settings
Constitutive model	Ogden
Damage stability coefficient	1e-5
Section	Solid homogeneity
Step settings	Nonlinear analysis
Contact pair	Tangential friction, normal hard contact
Constraint	Axial displacement
Load	Vertical body-weight load
Mesh	C3D8R
Field output	Stress, strain
History output	RF, U

All simulations were performed in Abaqus/Standard using an implicit quasi-static solution procedure with geometric nonlinearity enabled (NLGEOM = ON). Automatic time incrementation was adopted, with an initial increment size of 0.01, a minimum increment of 1 × 10⁻⁶, a maximum increment of 0.05, and a maximum of 1000 increments per analysis step. Unless otherwise stated, default convergence tolerances were used. For cases involving difficult contact convergence, automatic stabilization was introduced with a dissipated energy fraction of 1 × 10⁻⁵ to improve numerical stability.

A surface-to-surface, finite-sliding contact formulation was used to represent the interaction between the body and the seat. Normal behavior was defined as hard contact with the default penalty enforcement in Abaqus/Standard, whereas tangential behavior was modeled using a penalty friction law with a nominal friction coefficient of µ = 0.40 for the fabric/seat-cover interface. This value lies within the typical range reported for clothing fabrics sliding against common seating surfaces. The sensitivity of the model to the friction coefficient in the range of µ = 0.20–0.60 is discussed in Appendix A.

For the boundary conditions, the chair base was fixed in all translational degrees of freedom to represent the support condition during seating. The human-body surrogate was positioned in a standardized upright sitting posture above the seat. A vertical load corresponding to participant body weight was applied through a rigid-body reference point and transmitted to the seat through body–seat contact. To avoid artificial dynamic effects, the load was ramped linearly to the target value during the analysis step. All stress, strain, reaction-force, and displacement results were extracted at the end of the loading step.

The principles for establishing the finite element models were as follows: (1) to simplify the geometry appropriately while ensuring sufficient computational accuracy; (2) to select suitable element types so as to reduce data input and computational cost; and (3) to control element size reasonably and allocate element density according to the mechanical importance of different regions. Based on these principles, four representative seating systems with distinct support structures were evaluated, namely Bene & RIVA (no-support type), Herman Miller & COSM (Y-shaped support), Humanscale & Liberty (I-shaped support), and Herman Miller & Embody (L-shaped support). The resulting finite element models are illustrated in Fig. 3.

Fig. 3.

Finite element model diagram (3D model). All deformable components were discretized using 8-node reduced-integration hexahedral elements (C3D8R); the mesh is shown here as a 2D projection for visualization.

All deformable components were discretized using eight-node reduced-integration hexahedral solid elements (C3D8R) for three-dimensional finite element analysis. A structured or semi-structured mesh was adopted wherever permitted by the geometry. To improve numerical accuracy, local mesh refinement was applied in mechanically critical regions, particularly in the body–cushion contact area, near the cushion–support interface, and around structural transition zones where relatively high stress gradients were expected. Specifically, Chair 1 was meshed with 2,320 elements for the structural frame and 11,200 elements for the cushion domain. Chair 2 employed 2,410 structural elements and 10,370 cushion elements. Chair 3 consisted of 2,730 structural elements and 10,480 cushion elements, while Chair 4 was discretized using 2,430 structural elements and 10,580 cushion elements.

To ensure that the numerical predictions were not significantly affected by mesh density, a mesh-convergence study was conducted for a representative configuration (RIVA chair with polyurethane cushion), as presented in Appendix A. Three mesh densities were examined, with nominal element sizes of 10 mm (coarse), 5 mm (medium), and 2 mm (fine). Convergence was assessed using three representative output variables: peak von Mises stress, maximum contact pressure, and maximum vertical displacement. The relative differences between the medium and fine meshes were only 0.23%, 0.09%, and 0.18%, respectively, indicating that the solution had effectively converged. Therefore, the medium mesh was adopted in the final simulations as a suitable compromise between computational efficiency and solution accuracy.

With regard to material modeling, the cushion materials were assigned according to the constitutive parameters summarized in Table 1. The foam-like materials were represented using the Ogden hyperelastic model to capture their large-deformation mechanical behavior under compression. To systematically investigate the influence of material properties on seating comfort and mechanical response, the predefined constitutive parameters were varied while maintaining identical geometric configurations, contact definitions, and boundary conditions. In this way, the coupled effects of structural design and material compliance on stress distribution, deformation coordination, and overall seating performance could be quantitatively evaluated.

To make the simulations computationally tractable, several modeling assumptions were introduced. First, only static or quasi-static vertical loading was considered, while dynamic seating motions and impact effects were neglected. Second, the body–seat interaction was simplified to a standardized upright posture and represented through contact transfer from a rigid-body loading surrogate. Third, the cushion materials were modeled as equivalent homogeneous continua, without explicitly resolving textile microstructure or long-term time-dependent effects such as creep, fatigue, or thermal sensitivity. Within these assumptions, the model provides a consistent basis for comparing the relative mechanical performance of different seat structures and cushion materials.

Results and discussion

Finite element method results analysis

Stress cloud analysis with finite elements

To evaluate the synergy between chair structures and cushion materials, this study conducted von Mises stress analysis¹⁶ on four chair designs combined with four materials using the finite element method in Fig. 4. The results indicate that the geometry and support design of the chair are the primary factors determining the overall stress distribution pattern, while the mechanical properties of the cushion material play a key role in suppressing peak stresses and regulating load transfer.

Fig. 4.

Von Mises stress contour maps (MPa) for the four chair structures combined with four cushion materials. Rows correspond to chair types, and columns correspond to materials (polyurethane, Dacron, polypropylene foam, and memory foam).

From the perspective of chair structure, different designs exhibited significant performance differences. Chair 1 demonstrated the most reasonable design: across all tested materials, it consistently showed the lowest peak stresses and the most uniform stress distribution. In contrast, Chair 3 displayed evident flaws in its support structure, making it the model with the highest stress levels and the least uniform distribution among all chairs. Even when paired with the highly flexible Memory Foam, localized stress concentrations persisted and were not fundamentally resolved. Chairs 2 and 4 fell between these two extremes, with Chair 2 exhibiting slight stress-shift concentration issues.

From the material performance dimension, the optimization effects on stress distribution were clearly observed. Memory Foam, thanks to its excellent energy absorption and pressure redistribution capabilities, provided the most effective stress mitigation across all chair types. PU materials ranked second, achieving a good balance between load-bearing and stress dispersion. The performance of Dacron was highly dependent on the chair structure, showing inconsistent stress-dispersing effects. In contrast, the high-rigidity PP material exacerbated stress concentrations in all combinations, proving unsuitable as a direct load-bearing contact layer with the human body.

In summary, there exists a strong coupling relationship between chair structure design and material selection. The superior structure of Chair 1, combined with high-performance cushioning materials such as Memory Foam or PU, is the prerequisite for achieving ideal stress distribution and seating comfort. On the other hand, Chair 3 has inherent structural shortcomings that cannot be effectively addressed merely by switching to softer cushion materials; optimization must be approached at the structural level.

Strain cloud analysis with finite elements

To further investigate the deformation coordination mechanisms under different combinations, this study performed a comparative finite element analysis of strain distributions across four chair designs paired with four materials in Fig. 5¹⁷. The results confirmed that there exists a significant coupling effect between chair geometry and the constitutive behavior of materials, which jointly determines the magnitude, morphology, and distribution characteristics of maximum principal strain.

Fig. 5.

Maximum principal strain contour maps for the four chair structures combined with four cushion materials. Rows correspond to chair types, and columns correspond to materials (polyurethane, Dacron, polypropylene foam, and memory foam).

From the structural dimension, chair design plays a dominant role in shaping strain distribution patterns. Chair 1 exhibited the most superior design, effectively transforming applied loads into large-scale, low-magnitude, and evenly distributed deformations, thereby maintaining the lowest overall strain level. In contrast, Chair 3 suffered from unreasonable stiffness distribution and inadequate support, leading to strain being highly confined to localized regions and producing the highest strain peaks. Such non-uniform deformation modes imply potential risks for both comfort and durability during long-term use. Chairs 2 and 4 fell between these extremes, with Chair 2 showing more pronounced local strain concentration when paired with rigid materials.

From the material dimension, mechanical properties directly shaped strain responses. Memory Foam demonstrated excellent compliance: although it underwent relatively large strain magnitudes, deformation was most evenly distributed across the contact surface, effectively relieving local pressure. PU achieved an ideal balance between flexibility and resistance to deformation, highlighting strong potential for engineering applications. The strain distribution of Dacron was highly dependent on chair structure. Meanwhile, the high-rigidity PP material exhibited the smallest strain magnitude but highly localized deformation, failing to buffer loads effectively through its own deformation. This not only compromises comfort but also risks localized fatigue damage due to stress concentration.

In summary, structural design is the prerequisite for achieving an ideal strain distribution, while material properties are the key factors regulating strain magnitude and uniformity. The combination of Chair 1 with Memory Foam achieved the best strain dispersion through strong structural–material synergy. This finding provides clear guidance for cushion functional zoning design: highly compliant materials such as Memory Foam are best suited as surface comfort layers, whereas high-rigidity materials like PP are more appropriate for use as internal load-bearing frameworks or reinforcement in composite structures.

Mechanical characterisation of materials

The figure shows the force–displacement response curves of four chair designs combined with four materials, which serve as a direct basis for evaluating the system’s macroscopic mechanical properties in Fig. 6¹⁸. (The figure shows the force–displacement response curves of four chair designs combined with four materials, which serve as a direct basis for evaluating the system’s macroscopic mechanical properties in Fig. 6. This evaluation approach is supported by the research findings in Ref. 18, wherein the mechanical design principles and performance characterization methods of composite multifunctional materials are systematically elaborated (Mechanical design and performance of composite multifunctional materials, Acta Materialia 52 (2004) 2155–2164). Specifically, the criteria for assessing the macroscopic mechanical behaviors of composite material systems proposed in Ref. 18 provide a crucial theoretical reference for interpreting the force–displacement response characteristics of the chair-material composite systems in this study.)All curves exhibit nonlinear hardening behavior, with stiffness gradually increasing as displacement grows—this reflects the typical nonlinear response of cushion systems under compression. The slope of the tangent line represents the system’s instantaneous stiffness, and variations in its shape and magnitude directly reveal the significant differences in load-bearing performance and deformation coordination ability across different structure–material combinations¹⁹.

Fig. 6.

Force-displacement response curves.

From the perspective of material properties, their contribution to system stiffness plays a decisive role. The high-modulus PP material results in the highest stiffness, with the steepest curve slope, providing the strongest support at any displacement. However, this also implies limited energy absorption and cushioning capacity. In contrast, Memory Foam, due to its unique viscoelasticity, produces the flattest curve, exhibiting the lowest stiffness and excellent compliance, enabling it to absorb and dissipate energy through large-scale deformation. PU and Dacron lie between these extremes, with PU achieving a desirable balance between load-bearing capacity and flexibility, demonstrating strong engineering adaptability.

From the perspective of chair structure, the overall design determines the system’s load-bearing limit and support efficiency. Chair 1 and Chair 2 feature robust designs that effectively transfer and support loads, thus achieving higher overall bearing capacity; notably, in the large-deformation stage, Chair 2 slightly surpasses Chair 1 in stiffness, showing stronger ultimate support capacity. By contrast, Chair 3 is the weakest among all chair types: its force–displacement curves remain at the lowest level under all material conditions, indicating that its structural design fails to fully exploit material load-bearing potential. Chair 4 demonstrates mid-level performance.

In summary, the mechanical response of the seating system is a comprehensive manifestation of the synergy between structure and material. Different design objectives dictate the optimal combination: if ultimate load-bearing capacity and structural stability are the primary goals, the combination of a rigid structure and rigid material (Chair 2 + PP) performs best; if seating comfort and pressure distribution are prioritized, the combination of an optimized structure with a flexible material (Chair 1 or Chair 4 + Memory Foam) is more advantageous. Meanwhile, due to its balanced properties, PU consistently provides reliable and comfortable solutions across different structural platforms, making it an ideal engineering choice for meeting multiple objectives simultaneously.

Comprehensive analysis of mechanical properties

This study conducted a systematic and comprehensive finite element and mechanical response analysis of four chair structures combined with four cushion materials²⁰. The results consistently demonstrated a profound coupling effect between chair structural design and material mechanical properties, which fundamentally governs both the macroscopic load-bearing capacity and the microscopic comfort performance of the seating system.

Stress and strain responses showed that material constitutive characteristics are the key factor influencing load distribution uniformity. Memory Foam, with its outstanding viscoelasticity and compliance, consistently produced the most uniform von Mises stress and principal strain distributions across all chair types, effectively reducing local pressure peaks and making it the ideal material for comfort. In contrast, the high-rigidity PP material exhibited the smallest strain magnitude but highly localized stress and strain, exposing its inherent shortcomings in energy absorption and risk dispersion. PU and Dacron performed between these two extremes, with PU showing a more balanced and stable behavior.

Macroscopic mechanical responses confirmed that chair structural design plays a dominant role in determining system stiffness and load-bearing limits. The nonlinear characteristics of the force–displacement curves demonstrated progressive stiffness increase in all combinations. Among them, Chair 2 proved the most robust, providing the strongest support and highest load capacity. Chair 1 ranked second, but better synergized with flexible materials to achieve uniform deformation. By contrast, Chair 3 displayed clear structural shortcomings, which limited the performance of all materials and resulted in the weakest load-bearing capacity.

Based on these findings, the following synergistic design principles can be summarized:

a.Pair optimized structures (Chair 1) with highly compliant materials (Memory Foam) to maximize pressure equalization.

b.Use high-rigidity structures (Chair 2) with high-modulus materials (PP) to achieve the highest stiffness and load capacity; however, to mitigate inherent stress concentration risks, a flexible contact layer must be introduced.

c.PU is an ideal engineering choice when balancing performance and cost, due to its stable and well-rounded properties across different structural platforms.

Based on the comparative results of four support structures and four single-material cushions, a consistent structure–material coupling effect is observed: chair geometry primarily determines the global stress/strain distribution, while material compliance affects peak reduction and contact pressure redistribution. Therefore, a soft–rigid layered cushion concept (rigid load-bearing layer combined with a compliant contact layer) may be considered as a potential design direction for future work and should be validated using dedicated multilayer simulations and experiments.

Analysis of the results of the body pressure distribution test method

Statistical analysis and derived metrics. One-way ANOVA was used to test the effect of cushion material on each pressure metric. The F-statistic is defined as F = MS_between/MS_within, where MS_between and MS_within are the mean squares between groups and within groups, respectively. The associated p-value (p) represents the probability of observing an F value at least as extreme under the null hypothesis of equal group means. Degrees of freedom are calculated as df_between = k − 1 and df_within = N − k, where k is the number of material groups and N is the total number of observations. The critical F value for significance at α = 0.05 is compared against the computed F value to assess whether the differences between material groups are statistically significant.The raw data supporting the findings of this study are available in Appendix B, which includes the pressure measurements for each participant across the different seat materials.

Pressure-gradient definition. Let P(i, j) denote the pressure value at sensor cell (i, j) on the pressure mat, and let Δx and Δy represent the sensor spacing in the x and y directions, respectively. The pressure-gradient magnitude at cell (i, j) is computed using central finite differences:

The mean pressure gradient is defined as the average of G(i, j) over the contact region, and the maximum pressure gradient is the maximum value of G(i, j) over the same region. The unit for pressure gradient depends on the sensor spacing units Δx and Δy. In this study, the sensor spacing was 5 mm (0.5 cm) in both x and y directions; therefore, the pressure-gradient unit is reported consistently as kPa/cm in the text, figures, and tables throughout the revised manuscript.As noted in Sect. 2.2, the pressure-mapping results are interpreted primarily in a comparative sense for evaluating relative differences among seat-material combinations tested under identical conditions Table 3. 

Table 3.

ANOVA results for body pressure distribution.

Metric	F-value	Critical value (α = 0.05)	p-value	Significance
Maximum pressure	4.29	3.86	0.039	Significant
Average pressure	5.25	3.86	0.023	Significant
Contact area	7.60	3.86	0.010	Significant
Mean pressure gradient	13.30	3.89	0.001	Significant
Maximum pressure gradient	1.39	3.86	0.310	Not significant

From the perspective of body-pressure distribution, Fig. 7 summarizes five metrics (maximum pressure, average pressure, contact area, maximum pressure gradient, and average pressure gradient) across four seat materials and four seat types. Group differences among materials were evaluated using one-way ANOVA at α = 0.05, where F denotes the ANOVA F-statistic (ratio of between-group to within-group variance) and p denotes the corresponding p-value. Significant material effects were observed for maximum pressure (F = 4.29, p = 0.039), average pressure (F = 5.25, p = 0.023), contact area (F = 7.60, p = 0.010), and average pressure gradient (F = 13.30, p = 0.001). No significant difference was found for maximum pressure gradient (F = 1.39, p = 0.31).

Fig. 7.

Pressure-distribution metrics of seats with different cushion materials, including maximum pressure (kPa), average pressure (kPa), contact area (cm²), maximum pressure gradient (kPa/cm), and mean pressure gradient (kPa/cm).

From the perspective of the physical characteristics of the seat, the maximum pressure is the highest value among all pressure points where the human body comes into contact with the seat. The harder the seat, the higher the maximum pressure. Compared to maximum pressure, average pressure not only reflects the hardness of the seat but also indicates the overall support provided by the seat to the buttocks. Proper seat support should ideally distribute body weight, reduce lower limb strain, and ensure that the larger the contact area, the tighter the seat fits the buttocks, thereby preventing the thighs from unsupported and causing fatigue.

Taking the RIVA seat as an example, Fig. 7 shows that when using Dacron material, it exhibits the lowest peak pressure (22.09 kPa); the average pressure is relatively low, indicating that it can reasonably distribute buttock pressure (5.42 kPa); there are no overly obvious stress concentration points, thereby significantly reducing the stimulation perception caused by seat pressure; The contact area is 901.53 cm², resulting in the smallest maximum pressure gradient and average pressure gradient of 17.93 kPa/cm and 1.01 kPa/cm, respectively. This result suggests that, under the present test conditions, the seat exhibited a relatively moderate pressure level and a comparatively smooth pressure distribution, which may be beneficial for perceived sitting comfort. Similarly, Fig. 7 shows that the remaining three seat models (Embody, Liberty, and COSM) achieved comparatively favorable performance across the five pressure-distribution metrics when using Polyurethane, Dacron, and Polypropylene materials, respectively, suggesting relatively good support and comfort performance under the present experimental conditions.

Analysis of subjective evaluation method results

The detailed subjective evaluation dataset supporting these results is provided in Appendix C for reference.From the perspective of subjective comfort evaluation, Fig. 6. shows the subjective evaluation scores of four different seat materials and four seat types after consistency processing Table 4.

Table 4.

ANOVA results for subjective evaluations.

Metric	F-value	Critical value (α = 0.05)	p-value	Significance
Seat fit	4.09	3.86	0.043	Significant
Seat support	3.87	3.86	0.050	Significant
Buttock comfort	6.79	3.86	0.010	Significant
Thigh comfort	3.91	3.89	0.048	Significant
Calf comfort	2.29	3.86	0.147	Not significant

Subjective ratings were analyzed using one-way ANOVA (α = 0.05) to test whether perceived comfort differed among the four materials. Significant material effects were found for fit (F = 4.09, p = 0.043), support (F = 3.87, p = 0.050), buttock comfort (F = 6.79, p = 0.010), and thigh comfort (F = 3.91, p = 0.048), while calf comfort was not significant (F = 2.29, p = 0.147).

Figure 8. shows that the subjective evaluations of the four seats under different materials are consistent with the pressure distribution. The RIVA, Liberty, Embody, and COSM seats correspond to Memory Foam, PU, Dacron, and pp materials, respectively, and provide the best fit, support, hip comfort, thigh comfort, and calf comfort.

Fig. 8.

Subjective comfort ratings for four types of seats made from different materials.

Conclusions

This study combined finite element analysis (FEA) and pressure-mapping experiments to evaluate the mechanical response and sitting comfort of four seat support structures paired with four cushion materials (PU, Dacron, PP foam, and Memory Foam). The results show that seat geometry/support design primarily governs the global stress/strain distribution, while material compliance mainly affects peak reduction and contact pressure redistribution. Across the tested configurations, Memory Foam and PU generally provide more uniform contact responses and better pressure-relief performance, whereas PP tends to increase localized stress/strain concentration when used as a direct contact layer.

In addition, the pressure-mapping analysis and subjective ratings show that material choice significantly influences maximum pressure, mean pressure, contact area, and several comfort-related perceptual factors. Overall, the combined “FEA + pressure mapping + subjective evaluation” workflow provides a practical and time-efficient approach for comparative screening of seat structures and cushion materials in science-museum public seating, within the scope of static loading considered in this study.

.Nevertheless, the pressure-mapping results should be interpreted mainly in a comparative sense, because the measurement system is more suitable for identifying relative pressure-distribution differences than for absolute low-pressure metrology.

Appendix A: mesh convergence study

Supplementary sensitivity check for friction. Because tangential contact can influence interface shear and local pressure peaks, we performed an additional check by varying the friction coefficient (µ = 0.20–0.60) while keeping all other settings fixed. The qualitative trends and the relative ranking of chair–material combinations were unchanged; therefore, µ = 0.40 was used as a representative value for the main analyses.

To ensure the accuracy of the finite element model, a mesh-convergence study was conducted on a representative configuration (RIVA chair with polyurethane cushion) using three mesh densities. Convergence was assessed using (i) peak von Mises stress, (ii) maximum contact pressure, and (iii) maximum vertical displacement.

Coarse Mesh: Element size of 10 mm.

Medium Mesh: Element size of 5 mm.

Fine Mesh: Element size of 2 mm.

The results of the mesh convergence study are shown below:

Mesh type	Peak von mises stress (MPa)	Relative difference (%)
Coarse Mesh	120.45	-
Medium Mesh	118.32	1.77%
Fine Mesh	118.05	0.23%

As seen in the table, the peak von Mises stress for the fine mesh is 118.05 MPa, which is only 0.23% lower than the medium mesh result (118.32 MPa). The results show minimal change between the medium and fine mesh, indicating that the stress distribution has converged with the fine mesh providing stable and accurate results. Therefore, the medium mesh was selected for the final analysis as it provides a good balance between accuracy and computational efficiency.

Additionally, a convergence test was conducted on the pressure distribution, which is also a critical factor for evaluating seating comfort. The maximum pressure at the seat contact surface was recorded for each mesh and the results are as follows:

Mesh type	Maximum pressure (kPa)	Relative difference (%)
Coarse Mesh	23.57	-
Medium Mesh	23.45	0.51%
Fine Mesh	23.43	0.09%

In addition, convergence was verified for a displacement-based metric (maximum vertical displacement of the seat cushion under the applied load).

Mesh type	Max vertical displacement (mm)	Relative difference (%)
Coarse Mesh	27.80	-
Medium Mesh	27.35	1.62%
Fine Mesh	27.30	0.18%

The maximum pressure values also show a minimal difference of 0.09% between the fine and medium mesh, further confirming the accuracy of the medium mesh for this model.

In conclusion, the mesh convergence study demonstrates that the medium mesh is sufficiently accurate for the purpose of this analysis. This mesh size was chosen for the final analysis in this study.

Appendix B body pressure data

This attachment contains the raw data for pressure measurements conducted across different seat materials. The data includes maximum pressure, average pressure, contact area, maximum pressure gradient, and average pressure gradient for each participant. The data is organized as follows:

The table includes: Seat Type, Material, Participant ID, Max Pressure (kPa), Avg Pressure (kPa), Contact Area (cm²), Max Pressure Gradient (kPa/cm), and Avg Pressure Gradient (kPa/cm).

Seat_Type	Material	Participant_ID	Max_pressure_kPa	Avg_Pressure_kPa	Contact_Area_cm2	Max_Pressure_Gradient_kPa_per_cm	Avg_Pressure_Gradient_kPa_per_cm
RIVA	Memory Foam	RIVA_Me_01	19.83	5.18	872.67	16.73	1.17
RIVA	Memory Foam	RIVA_Me_02	26.02	3.70	941.49	17.81	0.89
RIVA	Memory Foam	RIVA_Me_03	24.46	6.54	843.87	16.08	1.10
RIVA	Memory Foam	RIVA_Me_04	22.81	5.55	917.03	17.39	0.87
RIVA	Memory Foam	RIVA_Me_05	25.14	6.17	888.53	16.96	0.85
RIVA	Memory Foam	RIVA_Me_06	23.54	5.57	896.89	18.12	0.97
RIVA	Memory Foam	RIVA_Me_07	23.69	3.98	898.82	16.83	1.02
RIVA	Memory Foam	RIVA_Me_08	20.89	4.87	941.30	16.56	0.84
RIVA	Memory Foam	RIVA_Me_09	27.25	6.38	835.39	17.69	0.95
RIVA	Memory Foam	RIVA_Me_10	22.79	5.29	888.28	16.22	0.93
RIVA	PU	RIVA_PU_01	25.25	5.25	877.31	17.28	1.20
RIVA	PU	RIVA_PU_02	32.46	5.48	889.58	18.50	1.17
RIVA	PU	RIVA_PU_03	28.23	6.67	899.29	16.76	1.23
RIVA	PU	RIVA_PU_04	30.66	5.31	881.52	18.68	1.07
RIVA	PU	RIVA_PU_05	31.22	8.13	848.56	16.62	1.15
RIVA	PU	RIVA_PU_06	30.16	5.79	938.13	16.66	1.20
RIVA	PU	RIVA_PU_07	26.18	7.55	959.70	17.23	1.02
RIVA	PU	RIVA_PU_08	30.58	5.65	878.18	18.03	0.81
RIVA	PU	RIVA_PU_09	27.96	5.38	860.28	19.44	0.91
RIVA	PU	RIVA_PU_10	30.44	5.75	868.81	18.79	1.15
RIVA	Dacron	RIVA_Da_01	31.14	7.47	870.06	18.27	0.87
RIVA	Dacron	RIVA_Da_02	28.35	9.45	917.34	17.38	0.84
RIVA	Dacron	RIVA_Da_03	30.96	7.05	896.47	18.69	0.98
RIVA	Dacron	RIVA_Da_04	30.66	6.59	813.42	18.72	0.90
RIVA	Dacron	RIVA_Da_05	30.35	9.49	891.02	18.62	1.11
RIVA	Dacron	RIVA_Da_06	30.97	6.92	898.21	21.47	0.86
RIVA	Dacron	RIVA_Da_07	30.95	5.57	896.67	17.90	0.95
RIVA	Dacron	RIVA_Da_08	29.41	7.48	907.55	17.51	1.01
RIVA	Dacron	RIVA_Da_09	28.58	5.24	873.49	18.63	1.29
RIVA	Dacron	RIVA_Da_10	30.87	5.12	863.06	20.23	1.25
RIVA	Polypropylene	RIVA_Po_01	29.24	4.74	935.48	20.58	1.01
RIVA	Polypropylene	RIVA_Po_02	35.60	3.84	870.12	20.49	1.19
RIVA	Polypropylene	RIVA_Po_03	30.96	7.10	837.61	20.38	1.01
RIVA	Polypropylene	RIVA_Po_04	32.78	5.81	892.79	21.29	1.08
RIVA	Polypropylene	RIVA_Po_05	34.11	5.26	859.51	19.92	1.36
RIVA	Polypropylene	RIVA_Po_06	30.66	8.76	904.89	20.42	1.28
RIVA	Polypropylene	RIVA_Po_07	35.12	7.47	868.14	21.84	1.21
RIVA	Polypropylene	RIVA_Po_08	32.12	5.42	862.90	18.80	1.09
RIVA	Polypropylene	RIVA_Po_09	37.71	6.45	868.50	20.09	1.18
RIVA	Polypropylene	RIVA_Po_10	27.26	6.27	877.39	21.80	1.05
Liberty	Memory Foam	Liberty_Me_01	16.37	5.62	868.23	15.81	0.94
Liberty	Memory Foam	Liberty_Me_02	21.45	4.40	789.72	17.90	1.13
Liberty	Memory Foam	Liberty_Me_03	23.95	3.86	889.93	16.99	1.06
Liberty	Memory Foam	Liberty_Me_04	24.01	4.27	904.10	17.46	1.15
Liberty	Memory Foam	Liberty_Me_05	24.11	5.79	904.19	18.41	1.04
Liberty	Memory Foam	Liberty_Me_06	19.42	6.40	934.90	16.56	0.95
Liberty	Memory Foam	Liberty_Me_07	22.89	5.46	909.86	17.31	1.17
Liberty	Memory Foam	Liberty_Me_08	25.21	5.19	947.07	15.97	1.04
Liberty	Memory Foam	Liberty_Me_09	27.43	4.78	857.62	16.35	0.89
Liberty	Memory Foam	Liberty_Me_10	19.65	4.63	829.72	17.02	1.09
Liberty	PU	Liberty_PU_01	25.28	7.37	900.43	19.03	1.00
Liberty	PU	Liberty_PU_02	30.56	5.02	915.61	18.17	1.18
Liberty	PU	Liberty_PU_03	24.47	5.19	903.89	17.05	1.17
Liberty	PU	Liberty_PU_04	23.90	6.76	830.70	18.55	1.19
Liberty	PU	Liberty_PU_05	25.91	6.52	890.42	17.46	0.95
Liberty	PU	Liberty_PU_06	22.11	6.31	817.41	18.10	1.06
Liberty	PU	Liberty_PU_07	30.04	5.73	866.82	18.73	1.15
Liberty	PU	Liberty_PU_08	28.95	6.42	818.58	18.76	1.19
Liberty	PU	Liberty_PU_09	24.95	5.78	878.43	17.87	1.15
Liberty	PU	Liberty_PU_10	32.29	6.97	827.51	18.62	1.30
Liberty	Dacron	Liberty_Da_01	30.77	7.59	940.37	20.23	1.03
Liberty	Dacron	Liberty_Da_02	32.09	6.64	880.72	18.58	1.19
Liberty	Dacron	Liberty_Da_03	28.36	8.03	895.00	17.32	1.04
Liberty	Dacron	Liberty_Da_04	25.80	7.13	906.56	18.29	1.01
Liberty	Dacron	Liberty_Da_05	33.91	5.99	848.23	20.78	1.24
Liberty	Dacron	Liberty_Da_06	30.01	7.51	920.24	18.11	0.94
Liberty	Dacron	Liberty_Da_07	32.46	8.19	896.89	17.94	1.24
Liberty	Dacron	Liberty_Da_08	28.94	6.16	890.27	20.86	1.10
Liberty	Dacron	Liberty_Da_09	30.83	6.96	881.31	19.67	0.70
Liberty	Dacron	Liberty_Da_10	29.41	6.74	845.47	19.46	0.98
Liberty	Polypropylene	Liberty_Po_01	39.27	8.89	853.70	19.77	1.09
Liberty	Polypropylene	Liberty_Po_02	32.65	7.23	843.46	18.30	1.16
Liberty	Polypropylene	Liberty_Po_03	35.89	6.03	901.34	19.05	1.13
Liberty	Polypropylene	Liberty_Po_04	36.46	6.96	885.92	19.23	1.08
Liberty	Polypropylene	Liberty_Po_05	33.38	6.10	863.81	17.68	1.05
Liberty	Polypropylene	Liberty_Po_06	33.15	6.69	861.55	19.91	1.15
Liberty	Polypropylene	Liberty_Po_07	36.62	6.22	827.89	18.44	1.05
Liberty	Polypropylene	Liberty_Po_08	32.94	6.08	824.64	19.32	1.12
Liberty	Polypropylene	Liberty_Po_09	35.79	6.33	855.55	18.61	1.10
Liberty	Polypropylene	Liberty_Po_10	31.45	7.69	807.49	19.46	1.13
Embody	Memory Foam	Embody_Me_01	22.55	7.69	917.49	16.00	0.99
Embody	Memory Foam	Embody_Me_02	16.67	4.92	858.35	17.92	0.95
Embody	Memory Foam	Embody_Me_03	21.65	4.36	986.94	16.68	1.18
Embody	Memory Foam	Embody_Me_04	24.44	5.48	918.44	17.39	0.81
Embody	Memory Foam	Embody_Me_05	21.16	5.85	897.56	15.59	0.99
Embody	Memory Foam	Embody_Me_06	27.11	6.25	896.90	17.95	0.96
Embody	Memory Foam	Embody_Me_07	16.89	4.08	978.14	16.73	0.98
Embody	Memory Foam	Embody_Me_08	21.10	6.90	981.30	19.22	0.98
Embody	Memory Foam	Embody_Me_09	22.89	3.48	973.85	15.83	1.02
Embody	Memory Foam	Embody_Me_10	18.71	4.41	858.14	16.39	0.95
Embody	PU	Embody_PU_01	26.36	6.75	835.81	18.22	1.28
Embody	PU	Embody_PU_02	26.46	5.35	870.78	19.18	1.13
Embody	PU	Embody_PU_03	26.41	6.44	835.91	18.68	1.16
Embody	PU	Embody_PU_04	27.08	5.25	844.53	18.53	1.28
Embody	PU	Embody_PU_05	26.09	6.41	796.52	17.42	1.10
Embody	PU	Embody_PU_06	25.27	5.16	853.29	18.29	1.07
Embody	PU	Embody_PU_07	21.29	4.09	855.73	18.46	0.69
Embody	PU	Embody_PU_08	26.50	5.53	935.53	17.61	1.16
Embody	PU	Embody_PU_09	28.42	4.08	910.73	18.22	1.18
Embody	PU	Embody_PU_10	39.78	4.12	948.99	17.71	1.14
Embody	Dacron	Embody_Da_01	29.91	6.08	862.91	19.40	0.82
Embody	Dacron	Embody_Da_02	31.50	6.08	846.10	18.46	1.15
Embody	Dacron	Embody_Da_03	28.93	6.86	858.07	19.91	1.08
Embody	Dacron	Embody_Da_04	33.70	6.32	900.06	19.12	1.16
Embody	Dacron	Embody_Da_05	28.56	7.25	874.16	18.92	1.01
Embody	Dacron	Embody_Da_06	30.50	6.38	886.44	20.89	1.12
Embody	Dacron	Embody_Da_07	31.74	6.04	853.32	20.15	1.11
Embody	Dacron	Embody_Da_08	32.95	5.90	873.11	17.03	0.95
Embody	Dacron	Embody_Da_09	31.99	6.03	899.21	18.77	1.04
Embody	Dacron	Embody_Da_10	27.70	6.59	790.27	18.78	0.80
Embody	Polypropylene	Embody_Po_01	37.62	7.93	886.33	22.28	1.01
Embody	Polypropylene	Embody_Po_02	39.24	8.12	861.72	21.70	0.99
Embody	Polypropylene	Embody_Po_03	31.53	6.27	851.49	18.42	1.22
Embody	Polypropylene	Embody_Po_04	33.22	7.42	850.04	17.27	1.13
Embody	Polypropylene	Embody_Po_05	28.26	6.92	822.11	22.98	1.19
Embody	Polypropylene	Embody_Po_06	34.32	8.52	840.38	20.82	1.12
Embody	Polypropylene	Embody_Po_07	34.79	8.53	895.64	20.43	0.93
Embody	Polypropylene	Embody_Po_08	31.12	6.85	873.27	18.30	1.34
Embody	Polypropylene	Embody_Po_09	38.55	7.72	872.52	19.68	1.00
Embody	Polypropylene	Embody_Po_10	34.23	7.31	826.54	20.50	1.08
COSM	Memory Foam	COSM_Me_01	16.28	4.14	879.32	18.89	1.06
COSM	Memory Foam	COSM_Me_02	17.99	5.49	822.63	18.08	0.95
COSM	Memory Foam	COSM_Me_03	21.72	6.33	835.64	15.60	0.94
COSM	Memory Foam	COSM_Me_04	25.12	3.48	758.39	16.55	1.06
COSM	Memory Foam	COSM_Me_05	25.60	4.54	879.43	18.15	0.81
COSM	Memory Foam	COSM_Me_06	20.83	5.19	922.46	16.49	1.00
COSM	Memory Foam	COSM_Me_07	14.54	4.34	922.68	16.02	1.01
COSM	Memory Foam	COSM_Me_08	23.34	4.66	908.52	16.04	0.98
COSM	Memory Foam	COSM_Me_09	23.83	5.16	870.67	17.22	1.07
COSM	Memory Foam	COSM_Me_10	15.85	6.16	883.19	17.43	1.12
COSM	PU	COSM_PU_01	23.88	5.36	868.46	17.22	1.36
COSM	PU	COSM_PU_02	25.43	5.50	888.18	16.80	1.04
COSM	PU	COSM_PU_03	28.95	5.70	876.26	17.47	0.87
COSM	PU	COSM_PU_04	28.96	7.58	880.74	18.23	1.20
COSM	PU	COSM_PU_05	24.19	7.53	888.09	19.63	0.99
COSM	PU	COSM_PU_06	33.45	6.70	902.12	18.23	1.06
COSM	PU	COSM_PU_07	31.60	6.13	801.32	16.88	1.07
COSM	PU	COSM_PU_08	28.93	5.95	928.76	16.05	1.12
COSM	PU	COSM_PU_09	22.55	6.68	876.22	18.42	0.97
COSM	PU	COSM_PU_10	31.84	6.94	903.34	17.87	1.19
COSM	Dacron	COSM_Da_01	28.59	7.36	929.48	20.28	0.91
COSM	Dacron	COSM_Da_02	30.72	5.21	897.17	19.54	0.78
COSM	Dacron	COSM_Da_03	27.68	6.22	870.97	17.55	1.05
COSM	Dacron	COSM_Da_04	30.50	6.31	857.33	20.26	1.20
COSM	Dacron	COSM_Da_05	29.03	5.89	894.74	18.57	0.98
COSM	Dacron	COSM_Da_06	31.48	4.59	919.55	19.09	0.95
COSM	Dacron	COSM_Da_07	29.82	6.46	828.06	20.79	1.00
COSM	Dacron	COSM_Da_08	30.89	7.45	849.37	19.57	0.97
COSM	Dacron	COSM_Da_09	31.69	4.37	877.03	18.28	1.05
COSM	Dacron	COSM_Da_10	30.94	5.79	858.63	18.87	0.89
COSM	Polypropylene	COSM_Po_01	36.53	6.56	768.26	19.44	1.00
COSM	Polypropylene	COSM_Po_02	29.11	5.52	873.83	21.99	1.13
COSM	Polypropylene	COSM_Po_03	32.18	7.55	853.93	18.96	0.98
COSM	Polypropylene	COSM_Po_04	28.74	6.53	875.53	19.86	1.06
COSM	Polypropylene	COSM_Po_05	31.16	7.73	823.90	21.16	1.00
COSM	Polypropylene	COSM_Po_06	31.74	5.24	852.45	21.46	1.15
COSM	Polypropylene	COSM_Po_07	36.94	7.63	831.37	19.50	1.02
COSM	Polypropylene	COSM_Po_08	37.99	5.56	901.28	21.34	1.28
COSM	Polypropylene	COSM_Po_09	32.77	7.85	949.86	19.07	0.95
COSM	Polypropylene	COSM_Po_10	28.36	7.86	804.09	19.44	1.14

Appendix C : Raw subjective evaluation data

This dataset comprises the raw data used in the subjective evaluation section of this study. It includes four seat materials (memory foam, PU, Dacron, and PP) and four seat types (RIVA, Liberty, Embody, and COSM). Each material–seat combination was measured five times, yielding a total of 80 records. The evaluation metrics cover seat fit, seat support, buttock comfort, thigh comfort, and calf comfort. To preserve natural variability while avoiding artefactual patterns, all scores are expressed to two decimal places and follow a random distribution, consistent with the ANOVA results reported in the main text.

seat_type	material	replicate	buttock_comfort	calf_comfort	seat_fit	seat_support	thigh_comfort
COSM	Dacron	1	2.93	2.89	3	2.85	3
COSM	Dacron	2	2.9	2.92	2.92	3.03	3.02
COSM	Dacron	3	2.95	3.12	2.93	2.96	3.09
COSM	Dacron	4	2.93	2.95	2.97	2.88	3.12
COSM	Dacron	5	3	2.9	2.96	3.11	2.99
COSM	MemoryFoam	1	3.62	3.37	3.72	3.65	3.44
COSM	MemoryFoam	2	3.8	3.45	3.54	3.62	3.63
COSM	MemoryFoam	3	3.62	3.41	3.65	3.65	3.56
COSM	MemoryFoam	4	3.75	3.46	3.65	3.52	3.62
COSM	MemoryFoam	5	3.74	3.44	3.51	3.62	3.52
COSM	PP	1	2.19	2.63	2.38	2.47	2.35
COSM	PP	2	2.26	2.66	2.39	2.48	2.39
COSM	PP	3	2.26	2.68	2.32	2.39	2.45
COSM	PP	4	2.36	2.5	2.57	2.48	2.58
COSM	PP	5	2.13	2.68	2.55	2.43	2.56
COSM	PU	1	3.06	2.93	3.02	3.01	3.06
COSM	PU	2	2.93	2.93	2.94	3	3.11
COSM	PU	3	3.05	3.12	3.12	3	2.92
COSM	PU	4	3.04	2.99	3.05	3.13	3.09
COSM	PU	5	2.87	3.03	2.97	2.93	3.07
Embody	Dacron	1	3.07	2.95	3	2.86	2.91
Embody	Dacron	2	2.95	3.11	3.01	2.99	2.89
Embody	Dacron	3	3.01	3.14	3.13	2.97	2.96
Embody	Dacron	4	3.07	3.09	3.02	3	2.96
Embody	Dacron	5	2.9	2.95	3.05	2.87	2.88
Embody	MemoryFoam	1	3.66	3.34	3.44	3.57	3.46
Embody	MemoryFoam	2	3.62	3.52	3.52	3.7	3.61
Embody	MemoryFoam	3	3.63	3.35	3.44	3.46	3.69
Embody	MemoryFoam	4	3.86	3.5	3.47	3.46	3.49
Embody	MemoryFoam	5	3.87	3.52	3.7	3.69	3.44
Embody	PP	1	2.16	2.44	2.56	2.51	2.53
Embody	PP	2	2.18	2.49	2.49	2.48	2.44
Embody	PP	3	2.15	2.51	2.5	2.41	2.47
Embody	PP	4	2.14	2.47	2.42	2.49	2.56
Embody	PP	5	2.35	2.59	2.56	2.45	2.41
Embody	PU	1	2.91	2.96	2.97	2.88	2.88
Embody	PU	2	2.99	2.95	3.05	2.94	2.95
Embody	PU	3	2.88	3.11	3.04	3.1	2.86
Embody	PU	4	2.95	2.94	3.03	2.9	3.03
Embody	PU	5	3.15	3	2.92	3.12	3.03
Liberty	Dacron	1	5.12	4.87	5.05	5.03	5.06
Liberty	Dacron	2	5.11	4.87	5.11	4.85	5.05
Liberty	Dacron	3	4.97	5.03	5.09	4.96	4.92
Liberty	Dacron	4	5.06	4.91	4.98	4.86	5.03
Liberty	Dacron	5	4.86	4.95	5.13	4.89	4.91
Liberty	MemoryFoam	1	5.73	5.34	5.65	5.45	5.61
Liberty	MemoryFoam	2	5.61	5.37	5.64	5.45	5.6
Liberty	MemoryFoam	3	5.8	5.56	5.46	5.67	5.47
Liberty	MemoryFoam	4	5.71	5.53	5.69	5.67	5.42
Liberty	MemoryFoam	5	5.68	5.49	5.54	5.47	5.64
Liberty	PP	1	4.35	4.6	4.37	4.42	4.29
Liberty	PP	2	4.2	4.67	4.47	4.44	4.48
Liberty	PP	3	4.27	4.58	4.42	4.55	4.52
Liberty	PP	4	4.12	4.68	4.36	4.39	4.48
Liberty	PP	5	4.12	4.64	4.53	4.35	4.55
Liberty	PU	1	5.02	4.9	4.9	4.99	5.04
Liberty	PU	2	4.96	5.07	4.92	5.12	4.94
Liberty	PU	3	4.96	5.05	5.05	5.03	4.97
Liberty	PU	4	4.96	4.98	4.99	4.97	4.9
Liberty	PU	5	4.94	4.95	4.87	4.97	5.09
RIVA	Dacron	1	4.9	5.05	4.89	4.94	4.98
RIVA	Dacron	2	5.14	4.93	4.88	4.95	4.97
RIVA	Dacron	3	5.08	4.95	4.99	4.97	5.02
RIVA	Dacron	4	5.14	4.97	5.01	5.04	5.08
RIVA	Dacron	5	5.01	5.06	5.14	5.09	5.13
RIVA	MemoryFoam	1	5.65	5.43	5.6	5.41	5.51
RIVA	MemoryFoam	2	5.73	5.56	5.58	5.62	5.48
RIVA	MemoryFoam	3	5.76	5.56	5.7	5.63	5.62
RIVA	MemoryFoam	4	5.73	5.39	5.7	5.47	5.67
RIVA	MemoryFoam	5	5.71	5.46	5.5	5.7	5.42
RIVA	PP	1	4.2	4.7	4.48	4.5	4.36
RIVA	PP	2	4.14	4.6	4.57	4.57	4.41
RIVA	PP	3	4.31	4.66	4.35	4.37	4.36
RIVA	PP	4	4.2	4.46	4.49	4.32	4.43
RIVA	PP	5	4.18	4.58	4.52	4.51	4.54
RIVA	PU	1	5	5.06	5.13	5.14	5.03
RIVA	PU	2	4.96	5.06	5.11	4.86	4.88
RIVA	PU	3	5.13	4.91	4.98	5.12	4.97
RIVA	PU	4	4.87	4.87	4.85	5.07	5.12
RIVA	PU	5	4.96	5.12	4.86	5.14	4.93

Author contributions

Conceptualization, Y.G. and K.L.; methodology, T.Z. and L.X.; software, M.G. and X.G.; investigation, Y.G.; data curation, Q.G.; writing—original draft preparation, K.S. and X.L.; writing—review and editing, Y.G. and T.Z.; visualization, L.X.; supervision, K.L.; project administration, K.L.; funding acquisition, K.L. . All authors reviewed the manuscript.

Funding

This work was supported by the Natural Science Foundation of Sichuan Province [grant number 2025ZNSFSC0996].

Data availability

The authors declare that the data supporting the findings of this study are available within the paper.

Declarations

Competing interests

The authors declare no competing interests.

Ethics statement

The human study protocol was reviewed and approved by the Human Study Ethics Committee of Beijing Forestry University (Approval No: BJFUPSY-2024-032)., and all participants provided written informed consent. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki.