# Exploring the Intersection of Ergonomics, Design Thinking, and AI/ML in Design Innovation
## 3 Results and Discussion
### Thematic Analysis of Selected Papers
#### 3.2.3 Main Findings and Implications  
The selected papers' main findings reveal significant insights into integrating AI/ML with design thinking and ergonomics in various domains. [PERSON] et al. [17] focused on developing regression and artificial neural network (ANN) models for predicting grip and key pinch strength using anthropometric data. The study's main finding that ANN models outperform regression models in accuracy underscores the potential of advanced AI techniques in ergonomic research. This finding is significant for industrial applications, where precise ergonomic designs are crucial for reducing musculoskeletal disorders. However, the reliance on quantitative measures may overlook individual variations in comfort and usability, suggesting a need for integrating qualitative feedback to achieve a more comprehensive ergonomic assessment. [PERSON] and [PERSON] [18] employed a three-phase method to generate innovative designs for motor scooter handle covers, validated through user surveys. Their finding that highly innovative concept designs are adaptable for market use highlights the effectiveness of combining user feedback with systematic design methodologies. This approach ensures that the final products are both innovative and user approved. Nonetheless, the study's dependence on user surveys might introduce bias, as users' self-reported preferences can sometimes be inconsistent with their actual behaviors and needs. [PERSON] and [PERSON] [19] demonstrated the capability of an AI-based chatbot to support socially sustainable design across various products. While their method proved effective, they also highlighted a significant limitation: the chatbot's filtering mechanism, which restricted the exploration of social consequences in design. This finding underscores the need for more flexible AI tools that allow designers to investigate their work's social impacts thoroughly. The study calls for further refinement of AI tools to better support sustainability goals in design. [PERSON] and [PERSON] [20] identified the need for improvement in digital human modeling (DHM) systems, particularly in accommodating emerging ergonomic trends and designing mobile work systems. Their systematic review reveals that current DHM systems fall short of addressing the complexities of modern ergonomic needs. This finding is critical as it points to gaps in existing technologies that must be addressed to enhance the efficacy of ergonomic interventions. Future research should focus on developing more advanced DHM systems that incorporate dynamic and context-specific ergonomic data. [PERSON], [PERSON], and [PERSON] [21] compared manual and AI-assisted design strategies in the fashion industry, finding that AI-assisted designs are preferable when innovation uncertainty is high and marginal costs are crucial. This finding emphasizes the strategic advantage of AI in managing design complexity and market variability. However, the study also suggests that manual design remains relevant in scenarios with significant fixed costs, highlighting the nuanced decision making required in adopting AI technologies. The dual approach offers a balanced perspective on the roles of AI and human designers in the creative process. [PERSON] [22] addressed the psychological and cognitive challenges of smart home integration, emphasizing the need for designs that are interoperable, reliable, and user aware. The study's findings highlight significant barriers to user acceptance and suggest that addressing these cognitive factors is essential for successfully adopting smart home technologies. This underscores the importance of human-centered design principles in developing smart technologies, ensuring they align with users' cognitive and psychological needs. [PERSON] [23] also adopted a mixed-methods approach, integrating neural networks to map key Kansei factors to product design elements.