Exploring the effect of walking patterns on pathway design in landscape architecture using gait analysis

  • Wanying Yang Xi’an University of Architecture and Technology, Xi’an 710055, China
  • Chao Wen Shanxi Province Architectural Design and Research Institute (GROUP) Co. Ltd, Xi’an 710000, China
  • Baogang Lin Xi’an University of Architecture and Technology, Xi’an 710055, China
Keywords: human walking patterns; biomechanics; walking mechanics; kinematic analysis; machine learning; landscape architecture; pathway slope
Ariticle ID: 435

Abstract

This study investigates the impact of pathway design on human walking patterns using advanced gait analysis techniques to inform landscape architecture. By analyzing key gait parameters such as stride length, cadence, walking speed, step width, and foot placement angles, this research seeks to identify how various pathway features—such as surface material, slope, curvature, and width—influence walking behaviour. Data is collected through motion capture systems and wearable sensors from diverse participants, including individuals of different ages and physical abilities. Statistical methods, including Multivariate Analysis of Variance (MANOVA), are applied to determine significant differences in walking patterns across pathway types, while ML techniques, such as k-means clustering, classify participants based on their walking strategies. The results offer data-driven insights into how different pathway designs affect walking efficiency and comfort. For example, pathways with a slope of 10% reduced WS by 14% compared to flat pathways, while surfaces like gravel increased Foot Placement Angles by 18% compared to concrete, impacting stability. The study provides practical recommendations for creating pathways that support natural human movement, such as ensuring step width and stride length remain consistent across varied surface types by designing smooth transitions between different materials. The study emphasizes the importance of designing inclusive, accessible pathways that accommodate the needs of diverse user groups. For instance, individuals with mobility challenges exhibited a 12% increase in step width on sloped surfaces, suggesting that gentler inclines and smoother textures are essential for accessibility. The findings contribute to LA by offering evidence-based guidelines that optimize pathways’ functionality and user experience in outdoor environments. These guidelines include maintaining a pathway slope below 5% for universal accessibility and using surface materials like concrete or permeable pavers that balance durability and comfort, promoting sustainability and user-centred design.

References

1. Loidl, H., & Bernard, S. (2022). Open (ing) spaces: Design as landscape architecture. Birkhäuser.

2. Chang, P. J. (2020). Effects of the built and social features of urban greenways on the outdoor activity of older adults. Landscape and Urban Planning, 204, 103929.

3. Coles, R., & Costa, S. (2023). Pathways, Nature Placings and Green Infrastructure. In Biophilic Connections and Environmental Encounters in the Urban Age (pp. 51-73). Routledge.

4. Saraswat, A. S. (2023). Designing Inclusive Spaces: Investigating the Role of Design in Creating Accessible Environments for People with Disabilities in the Context of Sustainable Development Goals. Journal for ReAttach Therapy and Developmental Diversities, 6(1), 1320-1333.

5. Dropkin, D., & Smith, N. (2021). Inclusive and accessible design. In Metric Handbook (pp. 4-1). Routledge.

6. Cox, B. (2024). Designing the streets of the future: The Avenues Programme, Glasgow (Doctoral dissertation, University of Glasgow).

7. Klöpfer-Krämer, I., Brand, A., Wackerle, H., Müßig, J., Kröger, I., & Augat, P. (2020). Gait analysis–Available platforms for outcome assessment. Injury, 51, S90-S96.

8. Wade, L., Needham, L., McGuigan, P., & Bilzon, J. (2022). Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ, 10, e12995.

9. Mobbs, R. J., Perring, J., Raj, S. M., Maharaj, M., Yoong, N. K. M., Sy, L. W., ... & Choy, W. J. (2022). Gait metrics analysis utilizing single-point inertial measurement units: A systematic review. Mhealth,

10. Uchida, T. K., & Delp, S. L. (2021). Biomechanics of movement: the science of sports, robotics, and rehabilitation. Mit Press.

11. Rana, M. S. (2024). Leveraging Markerless Computer Vision for Comprehensive Walking Automated Gait Analysis in Rehabilitation.

12. Schwartz, M. (2021). Human-centric accessibility graph for environment analysis. Automation in Construction, 127, 103557.

13. Park, H., Min, A., Lee, H., Shakeri, M., Jeon, I., & Woo, W. (2024, May). Comfortable Mobility vs. Attractive Scenery: The Key to Augmenting Narrative Worlds in Outdoor Locative Augmented Reality Storytelling. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-19).

14. Li, W., Xie, B., Zhang, Y., Meiss, W., Huang, H., & Yu, L. F. (2020). Exertion-aware path generation. ACM Trans. Graph., 39(4), 115.

15. Rana, M., & Mittal, V. (2020). Wearable sensors for real-time kinematics analysis in sports: A review. IEEE Sensors Journal, 21(2), 1187-1207.

16. Suo, X., Tang, W., Mao, L., & Li, Z. (2024). Digital human and embodied intelligence for sports science: advancements, opportunities and prospects. The Visual Computer, 1-17.

17. Pineo, H. (2022). Towards healthy urbanism: inclusive, equitable and sustainable (THRIVES)–an urban design and planning framework from theory to praxis. Cities & health, 6(5), 974-992.

18. Jian, I. Y., Chan, E. H., Xu, Y., & Owusu, E. K. (2021). Inclusive public open space for all: Spatial justice with health considerations. Habitat International, 118, 102457.

19. Harris, E., Franz, A., & O’Hara, S. (2023). Promoting social equity and building resilience through value-inclusive design. Buildings, 13(8), 2081.

20. Breloff, S. P., Carey, R. E., Wade, C., & Waddell, D. E. (2020). Inclination angles during cross-slope roof walking. Safety Science, 132, 104963.

21. Rasmussen, C. M. (2023). Slopes, Curves, and Falls: Expanding Our Grasp of Unconstrained Slip Recovery Beyond Straight, Level Gait (Doctoral dissertation, University of Nebraska at Omaha).

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. 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.

28. 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.

29. 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.

30. 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 .

31. 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.

32. 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.

33. 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

34. 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.

Published
2024-11-07
How to Cite
Yang, W., Wen, C., & Lin, B. (2024). Exploring the effect of walking patterns on pathway design in landscape architecture using gait analysis. Molecular & Cellular Biomechanics, 21(2), 435. https://doi.org/10.62617/mcb435
Section
Article