## 3 Results and Discussion
### Validation of Pressure Measurement: Experimental vs. Simulation-Based CP Prediction (Youn's Model)  
The pressure predictions from [PERSON]'s model were compared to the experimental pressure measurements obtained from the PPS and Kikubime sensors. [PERSON]'s model utilized simulation values obtained from CLO3D, including the local strain measurement and digitized stretch stiffness, applying a modified version of Laplace's law for pressure estimation [6]. Figure 5 shows that [PERSON]'s model aligned better with the experimental pressure results, particularly for the SJP and INTP fabrics, than the built-in CLO3D pressure values. For the SJP fabric stretched to 20%, [PERSON]'s model predicted pressure of approximately 5 kPa, whereas the CLO3D built-in feature predicted much higher pressure of over 11 kPa. Considering the experimental pressure range of 2.84 to 4.72 kPa, obtained from the PPS and Kikubime measurements, [PERSON]'s CP model provides a more accurate estimate. Similarly, for the INTP fabric stretched to 20%, [PERSON]'s CP model predicted pressure of around 5 kPa, while the CLO3D built-in feature estimated significantly higher pressure of 15 kPa. Given the experimental pressure range of 2.84 to 4.72 kPa, [PERSON]'s CP model again demonstrates  
Figure 5: Experimental pressure measurements (Kikuhimi, PPS) and predicted pressure values obtained from [PERSON]’s model using CLO3D. (**a**) single jersey, (**b**) single jersey plaited, (**c**) interlock, and (**d**) interlock plaited fabric. greater reliability. However, it is important to note that [PERSON]'s CP model also tended to slightly overestimate the contact pressure, although the predictions were much closer to the experimental values than those provided by the CLO3D built-in feature. This deviation may be attributed to the use of CLO3D's local strain values, which were generally higher than the experimental strain results. The exact predictive mechanisms within CLO3D's built-in pressure feature remain proprietary and unclear. Nonetheless, [PERSON]'s CP model, which incorporates factors such as the digitized stretch stiffness, fabric thickness, and garment circumference into a modified version of Laplace's law, appears to offer a more suitable approach to predicting the pressure in compression garments than the pressure directly simulated by CLO3D. Additionally, the deviation between [PERSON]'s CP model and the experimental pressure obtained from the two types of sensors may be acceptable, given that pressure sensors themselves can have a standard deviation of up to 0.22 (pps) or 0.49 kPa (ikkuhimi). This variation could be due to differences in the pressure distribution across the fabric surface and interactions with the manikin's body stiffness. Practical Application in Medical Compression Band Design: Integration of Virtual Fit Technology and Electrode Composites  
To validate the use of virtual fit technology in the design of medical compression waistbands, four new samples--SJ2, INT2, SJP2, and INTP2--were developed, distinct from those used in the previous strain and pressure assessments (SJ, INT, SJP, and INTP). A unique pattern design was developed specifically for this study, as illustrated in Figure 6a. The design incorporated electrode composites, adding functionality to the waistband by enabling e-textile features such as heating, a useful component in therapeutic compression applications [19; 20]. Figure 6b,c depict the experimental setup for both the physical and virtual strain measurements of the waistband and composite. The waistband's dimensions could be adjusted using Velcro, reducing the fabric width by 5%, 10%, 15%, and 20% (Figure 6d). Figure 6: (**a**) Illustration of compressional band pattern design. (**b**) Experimental setup with manikin wearing the compressional band integrated with the electrode composite. (**c**) A 3D-scanned manikin visualized in CLO3D wearing the simulated compressional band with composite.