# Streamlining Haptic Design with Micro-Collision Haptic Map Generated by Stable Diffusion +
## 4 User Study: Streamlining Haptic Design with Generative MCHa Map
### Result
#### 4.6.2 User Evaluation  
To assess the quality of the design outputs, we conducted Friedman tests followed by Nemenyi post hoc comparisons focusing on two key comparisons: (1) professional haptic using traditional tools (GP) versus our methods (GPG), and (2) professional haptic (GPG) versus non-haptic designers (GUG) using our methods (Figure 8).  
For **Overall** ratings, the Friedman test revealed a significant difference (\(\chi^{2}(2)=10.23\), \(p=0.0060\)). Post hoc analysis showed that designs created with the intelligent tool by professional designers (GPG) were rated significantly higher than those created with traditional tools (GP) (\(p=0.0084\)), while no significant difference was found between GPG and GUG (\(p=0.4002\)). For **Consistency**, a significant difference was observed (\(\chi^{2}(2)=40.63\), \(p<0.0001\)). GPG designs were rated significantly more consistent than GUG (\(p<0.0001\)), although no significant difference was found between GP and GPG (\(p=0.1469\)).  
Figure 8: The result of the user rating score. This figure shows the result of the user rating score. The deep red represents the group of professional haptic designers with generative MCHa (GPG). The light red represents the group of professional haptic designers with traditional methods (GP). The deep blue represents the group of non-haptic designers with our MCHa methods (GUG). The light blue represents the group of non-haptic designers with traditional methods (GU). Bar heights represent the means; significance levels are indicated with asterisks.  
Figure 7: The result of workload. This figure shows the result of workload. The deep red represents the group of professional haptic designers with generative MCHa (GPG). The light red represents the group of professional haptic designers with traditional methods (GP). The deep blue represents the group of non-haptic designers with our MCHa methods (GUG). The light blue represents the group of non-haptic designers with traditional methods (GU). Bar heights represent the means; significance levels are indicated with asterisks.  
Regarding **Gravel Realism**, the Friedman test indicated a significant difference (\(\chi^{2}(2)=22.04\), \(p<0.0001\)). GPG designs achieved significantly higher realism ratings compared with GP (\(p=0.0002\)), whereas no significant difference was observed between GPG and GUG (\(p=0.5561\)). For **Metal Realism**, no significant difference was found among groups (\(\chi^{2}(2)=3.95\), \(p=0.1385\)). For **Fabric Realism**, a significant difference was detected (\(\chi^{2}(2)=8.25\), \(p=0.0161\)). GPG designs outperformed GP with a significant difference (\(p=0.0222\)), and there was no significant difference between GPG and GUG (\(p=0.4375\)). For **Wood Realism**, the Friedman test again showed significant differences (\(\chi^{2}(2)=9.88\), \(p=0.0072\)). GPG designs were significantly preferred to GP (\(p=0.0184\)), while no significant difference was found between GPG and GUG (\(p=0.8303\)).  
Finally, for **Discriminability** (identification accuracy) (Figure 9), a highly significant difference was observed (\(\chi^{2}(2)=34.94\), \(p<0.0001\)). Designs produced by professional designers with the intelligent tool (GPG) were rated significantly higher than those using traditional methods (GP) (\(p<0.0001\)), and no significant difference was observed between GPG and GUG (\(p=0.5561\)).