Exploring the influence of body movements on spatial perception in landscape and interior design

  • Pengfei Zhao Academy of Art and Design, Sichuan University of Culture and Arts, Mianyang 621000, China
Keywords: body movements; embodied cognition; virtual reality; spatial perception; motion tracking
Article ID: 434

Abstract

This study investigates the influence of body movements on spatial perception in both landscape and interior design environments, focusing on how physical interactions shape spatial understanding beyond visual perception alone. Grounded in the theory of embodied cognition, the research examines how gait, posture, and movement dynamics affect spatial awareness. The study captures detailed data on movement patterns and visual engagement across different spatial contexts using a combination of real-world observations and Virtual Reality (VR) simulations, motion-tracking systems, wearable sensors, and eye-tracking technology. A total of 157 participants, aged 20 to 65, navigated both outdoor landscapes and indoor environments, with key variables such as surface materials, spatial layout, and lighting conditions manipulated to assess their effects on spatial perception. The study measured gait speed, step frequency, path deviations, time to destination, visual attention, and subjective ratings of perceived openness, ease of movement, and emotional response. Key findings include that surface materials significantly influenced gait speed and step frequency. For example, participants walking on concrete had a significantly faster gait speed (mean difference = 0.5220, p = 0.001) than those walking on gravel. In terms of spatial layout, the two-way Analysis of variance (ANOVA) results showed that winding paths led to more path deviations (F-statistic = 350.00, p = 3.19 × 10−8) and longer times to destination (F-statistic = 1744.00, p = 2.39 ´ 1011) compared to straight paths. The environment type (landscape vs. interior) also significantly affected navigation, with landscape participants showing a more significant deviation from direct paths (F-statistic = 19.60, p = 2.37 × 10−3). Visual engagement data, analyzed through a chi-square test, indicated that vertical elements like walls approached significance in attracting visual attention (Chi-square = 2.88, p = 0.0896), while other elements like trees and benches had less impact. The Wilcoxon signed-rank test results showed significant differences between real-world and VR experiences in perceived openness (W-statistic = 0.0, p = 0.001953), ease of movement (W-statistic = 0.0, p = 0.001953), and comfort (W-statistic = 0.0, p = 0.001953), highlighting VR’s limitations in replicating the full embodied experience of physical spaces.

References

1. Dalay, L. The impact of biophilic design elements on the atmospheric perception of the interior space. Uluslararası Peyzaj Mimarlığı Araştırmaları Dergisi (IJLAR) E-ISSN: 2602-4322. 2020, 4(2), 4-20.

2. Liu, M., Nijhuis, S. Mapping landscape spaces: Methods for understanding spatial-visual characteristics in landscape design. Environmental Impact Assessment Review. 2020, 82, 106376.

3. Benyon, D. Spaces of interaction, places for experience. Springer Nature. 2022.

4. Salingaros, N. A. Rules for urban space: design patterns create the human scale. Journal of Urban Research and Development. 2021, 2(1), 4-16.

5. Khatin-Zadeh, O., Eskandari, Z., Cervera-Torres, S., Ruiz Fernández, S., Farzi, R., & Marmolejo-Ramos, F. (2021). The strong versions of embodied cognition: Three challenges faced. Psychology & Neuroscience, 14(1), 16.

6. Farina, M. Embodied cognition: dimensions, domains and applications. Adaptive Behavior. 2021, 29(1), 73-88.

7. Fuchs, T. The circularity of the embodied mind. Frontiers in Psychology. 2020, 11, 1707.

8. Reed, C. L., Hartley, A. A. Embodied attention: Integrating the body and senses to act in the world. Handbook of Embodied Psychology: Thinking, Feeling, and Acting. 2021, 265-290.

9. Bailey, E. K. Becoming-Cyborg in Outdoor Spaces. The George Washington University; 2024.

10. Sinnamon, C., Miller, E. Architectural concept design process impacted by body and movement. International Journal of Technology and Design Education. 2022, 1-24.

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. Hutomo, S., Fuad, H. Engagement and well-being in public space. Case study: Suropati Park Jakarta. 2020.

25. Wenzel, E. M., Godfroy-Cooper, M. The role of tactile cueing in Multimodal displays: application in Complex task environments for space exploration. 2021.

26. Elver Boz, T., Demirkan, H., Urgen, B. A. Visual perception of the built environment in virtual reality: A systematic characterization of human aesthetic experience in spaces with curved boundaries. Psychology of Aesthetics, Creativity, and the Arts. 2022.

27. Ching, F. D. Architecture: Form, space, and order. John Wiley & Sons. 2023.

28. Loidl, H., & Bernard, S. Open (ing) spaces: Design as landscape architecture. Birkhäuser; 2023.

29. Salingaros, N. A. Rules for urban space: design patterns create the human scale. Journal of Urban Research and Development. 2021, 2(1), 4-16.

30. Salingaros, N. A. Planning, complexity, and welcoming spaces: The case of campus design. In Handbook on Planning and Complexity (pp. 353-372). Edward Elgar Publishing; 2020.

31. Van Aswegen, A. Disruption by dissociation: exploring human-centred design through transformative engagement in the spatial design studio (Doctoral dissertation, University of Pretoria); 2021.

32. Phillips, J. D. Landscape evolution: landforms, ecosystems, and soils. Elsevier. 2021.

33. Ullerup Mathers, E. The Impact of Nature-Based Sensory Experiences on Outdoor Behavior. 2022.

34. Allen, R. Grounded: How connection with nature can improve our mental and physical wellbeing. Hachette UK. 2021.

35. Minucciani, V., Saglar Onay, N. (Eds.). Well-being design and frameworks for interior space. IGI Global; 2020.

36. Goossens, S., Wybouw, N., Van Leeuwen, T., Bonte, D. The physiology of movement. Movement Ecology. 2020, 8, 1-13.

37. Korkut, E. H., & Surer, E. (2023). Visualization in virtual reality: a systematic review. Virtual Reality, 27(2), 1447-1480.

38. de Freitas, F. V., Gomes, M. V. M., Winkler, I. Benefits and challenges of virtual-reality-based industrial usability testing and design reviews: A patents landscape and literature review. Applied Sciences. 2022, 12(3), 1755.

39. Bigazzi, R., Landi, F., Cornia, M., Cascianelli, S., Baraldi, L., Cucchiara, R. Out of the box: embodied navigation in the real world. In Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, September 28–30, 2021, Proceedings, Part I 19 (pp. 47-57). Springer International Publishing.

Published
2024-11-08
How to Cite
Zhao, P. (2024). Exploring the influence of body movements on spatial perception in landscape and interior design. Molecular & Cellular Biomechanics, 21(3), 434. https://doi.org/10.62617/mcb434
Section
Article