A biomechanics-oriented study on the impact of AIGC on user interaction and ergonomics in visual communication design
Abstract
This study investigates the biomechanical implications and ergonomic impacts of AI-generated content (AIGC) integration in visual communication design workflows. Through a comprehensive analysis of 23 professional designers in Chengdu, China, we examined the physical stress patterns, user interaction dynamics, and overall ergonomic outcomes when transitioning from traditional to AIGC-assisted design processes. The research employed a mixed-method approach combining quantitative biomechanical measurements with qualitative user experience assessments over 12 weeks. Results revealed significant reductions in muscle activity across key muscle groups, with the upper trapezius showing the most significant decrease (−3.6% MVC, p < 0.001) during AIGC-assisted tasks. This change in muscle activity can be further linked to alterations in the body's postural stability and load distribution, which are core considerations in biomechanics. Movement efficiency metrics, which are inherently related to biomechanical performance, demonstrated a 27.9% reduction in task completion time (p < 0.001) and a 33.3% decrease in design iterations. Quality assessment scores improved across all dimensions, with Creative Innovation showing the highest enhancement (+1.8 points, p < 0.001). User satisfaction metrics indicated significant improvements, with consistent gains of 1.1 points (on a 5-point scale) across all measured dimensions (p < 0.001). Notably, the study identified distinct adaptation patterns between novice and experienced users in terms of their biomechanical responses. Experienced users demonstrated significantly faster response times in AIGC prompt input (8.94 ± 1.87 s vs 18.62 ± 3.15 s, p < 0.001), which can be associated with differences in their neuromuscular coordination and motor learning abilities. While AIGC integration initially increased certain types of errors (+51.2% in input errors), it led to substantial reductions in tool misuse (−40.4%) and design revisions (−39.9%). These findings suggest that AIGC integration can significantly reduce physical stress while improving design efficiency and quality outcomes, all of which are intertwined with the biomechanical functioning of the body during the design process. The research provides evidence-based recommendations for optimizing AIGC implementation in professional design workflows, taking into account the biomechanical and ergonomic factors that contribute to the overall well-being and creative productivity. This study has important implications for software development, workplace health policies, and the future direction of AI-assisted creative work, as it highlights the significance of considering biomechanics in the integration of advanced technologies within creative domains.
References
1. Onyejelem, T. E., & Aondover, E. M. (2024). Digital Generative Multimedia Tool Theory (DGMTT): A Theoretical Postulation in the Era of Artificial Intelligence. Adv Mach Lear Art Inte, 5(2), 01-09.
2. Huang, L., & Zheng, P. (2023). Human-computer collaborative visual design creation assisted by artificial intelligence. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(9), 1-21.
3. Aris, S., Aeini, B., & Nosrati, S. (2023). A digital aesthetics? Artificial intelligence and the future of the art. Journal of Cyberspace Studies, 7(2), 219-236.
4. Wang, J., & Liang, J. (2023). The Influences of AI and Emerging Technologies on the Imagery Stock Industry as Exemplified by the Visual China Group. Emerging Media, 1(2), 146-163.
5. Provasi, V. (2023). The AI revolution: evaluating impact and consequences in copywriting (Doctoral dissertation).
6. Chng, L. K. (2023). How AI makes its mark on instructional design. Asian Journal of Distance Education, 18(2), 32-41.
7. Biele, C. (2022). Human Movements in Human-Computer Interaction (HCI).
8. Escamilla Pinilla, A. (2024). Motion-based feature analysis for the design of full-body interactions in the context of computer vision and large volume spaces.
9. Mixter, S., Mathiassen, S. E., Jahncke, H., Hygge, S., Lyskov, E., Hallman, D. M., & Lewis, C. (2023). Effects of combining occupationally relevant physical and cognitive tasks. A systematic review. Annals of work exposures and health, 67(3), 303-319.
10. Asadi, N., & Sadeghi-Yarandi, M. Investigating the Relationship Between Environmental and Cognitive Ergonomics with Work-Related Musculoskeletal Disorders: A Case Study in an Automobile Industry. Work, (Preprint), 1-16.
11. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI:10.3390/s23094365.
12. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI:10.1109/ACCESS.2023.3236843.
13. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI:10.1007/s10772-022-09960-1.
14. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI:10.1016/j.seta.2021.101601.
15. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI:10.1016/j.avb.2021.101633.
16. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI:10.1007/s11042-021-10842-y.
17. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI:10.1007/s11277-021-08333-2.
18. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI:10.1007/s12652-020-02869-0.
19. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors, and Microsystems, 83, 2021, 104030, DOI:10.1016/j.micpro.2021.104030.
20. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.
21. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563-574, https://doi.org/10.53759/7669/jmc202404054.
22. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x
23. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI:10.1007/978-981-19-7455-7_57.
24. Wu, J., Cai, Y., Sun, T., Ma, K., & Lu, C. (2024). Integrating AIGC with design: dependence, application, and evolution-a systematic literature review. Journal of Engineering Design, 1-39.
25. Zhang, M., Zhang, X., Chen, Z., Wang, Z., Liu, C., & Park, K. (2024). Charting the path of technology-integrated competence in industrial design during the era of Industry 4.0. Sustainability, 16(2), 751.
26. Luo, Y., & Wang, L. (2023, September). Crisis and Responses to Design and Design Education in the AIGC Era. In ICEKIM 2023: Proceedings of the 4th International Conference on Education, Knowledge and Information Management, ICEKIM 2023, May 26–28, 2023, Nanjing, China (Vol. 469). European Alliance for Innovation.
27. Hughes, R. T., Zhu, L., & Bednarz, T. (2021). Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends—frontiers in artificial intelligence, 4, 604234.
28. Sindiramutty, S. R., Prabagaran, K. R. V., Akbar, R., Hussain, M., & Malik, N. A. (2025). Generative AI for Secure User Interface (UI) Design. In Reshaping CyberSecurity With Generative AI Techniques (pp. 333-394). IGI Global.
29. Li, F., Mostajeran, F., Steinicke, F., & Browning, M. H. (2024). Extended Reality in Environmental Neuroscience Research. In Environmental Neuroscience (pp. 455-490). Cham: Springer Nature Switzerland.
30. Ghorbani, M. A. (2023). AI tools to support design activities and innovation processes.
31. Lorenzini, M., Lagomarsino, M., Fortini, L., Gholami, S., & Ajoudani, A. (2023). Ergonomic human-robot collaboration in industry: A review. Frontiers in Robotics and AI, 9, 813907.
32. Gajdzik, B., & Wolniak, R. (2022). Smart production workers in terms of creativity and innovation: The implication for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(2), 68.
33. Hasanain, B. (2024). The Role of Ergonomic and Human Factors in Sustainable Manufacturing: A Review. Machines, 12(3), 159.
Copyright (c) 2025 Yiwen Chen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
