Biomechanical model study on the effect of floor materials on walking stability in tea space design
Abstract
Floor materials have a considerable impact on walking stability, especially in tea spaces where quiet and comfort are crucial. The materials used have an impact on users’ biomechanics, which influences balance, postural stability, and overall enjoyment in these places. Despite their importance, few studies have looked into the biomechanical impacts of floor materials in such environments. The purpose of this research is to create a biomechanical model to assess the impact of various floor surfaces on walking stability in tea space design, with the use of artificial intelligence (AI) for prediction. A biomechanical model using AI algorithms was used to simulate walking movements on different floor materials. The model predicts walking stability using friction, surface texture, and material hardness. The data were acquired using motion capture and sensor technology; data from people walking on surfaces like wood, ceramic tiles, and tatami mats were obtained and pre-processed by data cleaning, and z-score normalization, extracting features using Principal Component Analysis (PCA). The trained data are processed using Dynamic Grasshopper Optimized Deep Belief Network (DGO-DBN) techniques to improve forecast accuracy. The results show that wooden and tatami surfaces are more stable than ceramic tiles, which have a higher risk of slips and trips. The findings highlight the necessity of appropriate material selection in tea space planning to improve walking stability and reduce safety issues. This research offers light on how biomechanical analysis, paired with AI, might influence better design decisions for spaces that promote user comfort and safety.
References
1. Su, X. and Zhang, H., 2022. Tea drinking and the tastescapes of wellbeing in tourism.Tourism Geographies,24(6-7), pp.1061-1081.
2. Yoon, J., Lee, B., Chun, J., Son, B. and Kim, H., 2022. Investigation of the relationship between Ironworker’s gait stability and different types of load carrying using wearable sensors.Advanced Engineering Informatics,51, p.101521.
3. Ni, S. and Ishii, K., 2023. The relationship between consumer behavior and subjective well-being in Chinese teahouses and cafes: A social capital perspective.Journal of Leisure Research,54(4), pp.429-452.
4. Xue, X., Wang, H., Xie, J., Gao, Z., Shen, J. and Yao, T., 2023. Two-dimensional biomechanical finite element modeling of the pelvic floor and prolapse.Biomechanics and Modeling in Mechanobiology,22(4), pp.1425-1446.
5. Trageser, N., Sauerwald, A., Ludwig, S., Malter, W., Wegmann, K., Karapanos, L., Radosa, J., Jansen, A.K. and Eichler, C., 2022. A biomechanical analysis of different meshes for reconstructions of the pelvic floor in the porcine model.Archives of Gynecology and Obstetrics, pp.1-9.
6. Uchida, T.K. and Delp, S.L., 2021.Biomechanics of movement: the science of sports, robotics, and rehabilitation. Mit Press.
7. Mashhouri, L., 2021. Applying safe flooring in housing environments related to the independent elderly: evaluating suitability flooring technology to absorb impact in the event of a fall.
8. Baobeid, A., Koç, M. and Al-Ghamdi, S.G., 2021. Walkability and its relationships with health, sustainability, and livability: elements of the physical environment and evaluation frameworks. Frontiers in Built Environment,7, p.721218.
9. Huang, Y., Yao, K., Zhang, Q., Huang, X., Chen, Z., Zhou, Y. and Yu, X., 2024. Bioelectronics for electrical stimulation: materials, devices and biomedical applications.Chemical Society Reviews.
10. Ding, Y., Pang, Z., Lan, K., Yao, Y., Panzarasa, G., Xu, L., Lo Ricco, M., Rammer, D.R., Zhu, J.Y., Hu, M. and Pan, X., 2022. Emerging engineered wood for building applications.Chemical Reviews,123(5), pp.1843-1888.
11. Hoffmann, R., Brodowski, H., Steinhage, A. and Grzegorzek, M., 2021. Detecting walking challenges in gait patterns using a capacitive sensor floor and recurrent neural networks.Sensors,21(4), p.1086.
12. Lu, Z., Sun, D., Xu, D., Li, X., Baker, J.S. and Gu, Y., 2021. Gait characteristics and fatigue profiles when standing on surfaces with different hardness: Gait analysis and machine learning algorithms.Biology,10(11), p.1083.
13. Alharthi, A.S., Casson, A.J. and Ozanyan, K.B., 2021. Spatiotemporal analysis by deep learning of gait signatures from floor sensors.IEEE Sensors Journal,21(15), pp.16904-16914.
14. Shi, Q., Zhang, Z., He, T., Sun, Z., Wang, B., Feng, Y., Shan, X., Salam, B. and Lee, C., 2020. Deep learning enabled smart mats as a scalable floor monitoring system.Nature communications,11(1), p.4609.
15. Lattanzi, E. and Freschi, V., 2020. Evaluation of human standing balance using wearable inertial sensors: a machine learning approach.Engineering Applications of Artificial Intelligence,94, p.103812.
16. Anderson, W., Choffin, Z., Jeong, N., Callihan, M., Jeong, S. and Sazonov, E., 2022. Empirical study on human movement classification using insole footwear sensor system and machine learning.Sensors,22(7), p.2743.
17. Fagert, J., Mirshekari, M., Pan, S., Lowes, L., Iammarino, M., Zhang, P. and Noh, H.Y., 2021. Structure-and sampling-adaptive gait balance symmetry estimation using footstep-induced structural floor vibrations.Journal of Engineering Mechanics, 147(2), p.04020151.
18. Promsri, A., Cholamjiak, P. and Federolf, P., 2023. Walking stability and risk of falls.Bioengineering,10(4), p.471.
19. Alfuth, M., Ebert, M., Klemp, J. and Knicker, A., 2021. Biomechanical analysis of single-leg stance using a textured balance board compared to a smooth balance board and the floor: A cross-sectional study.Gait & posture,84, pp.215-220.
20. Singer, H. and Özşahin, Ş., 2022. Prioritization of laminate flooring selection criteria from experts’ perspectives: a spherical fuzzy AHP-based model.Architectural Engineering and Design Management,18(6), pp.911-926.
21. Thies, S.B., Bates, A., Costamagna, E., Kenney, L., Granat, M., Webb, J., Howard, D., Baker, R. and Dawes, H., 2020. Are older people putting themselves at risk when using their walking frames?.BMC geriatrics,20, pp.1-11.
22. Qiu, J. and Liu, H., 2021. Gait Recognition for Human‐Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition.Mathematical Problems in Engineering,2021(1), p.5039285.
23. Huang, H., Lin, X., Zhang, J., Wu, Z., Wang, C. and Wang, B.J., 2021, August. Performance of the hollow-core cross-laminated timber (HC-CLT) floor under human-induced vibration. InStructures(Vol. 32, pp. 1481-1491). Elsevier.
24. Rátonyi, D., Koroknai, E., Pákozdy, K., Sipos, A.G., Takacs, P., Krasznai, Z.T. and Kozma, B., 2024. The impact of short-term pelvic floor muscle training on the biomechanical parameters of the pelvic floor among patients with stress urinary incontinence: A pilot study.European Journal of Obstetrics & Gynecology and Reproductive Biology,302, pp.283-287.
25. Nouredanesh, M., Godfrey, A., Powell, D. and Tung, J., 2022. Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment.Journal of neuroengineering and rehabilitation,19(1), p.79.
26. Ziya. Walking Stability Data on Various Floor Materials—Biomechanical Data for Analyzing Walking Stability.Available online: https://www.kaggle.com/datasets/ziya07/walking-stability-data-on-various-floor-materials (accessed on 2 November 2024).
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