Hostname: page-component-848d4c4894-x24gv Total loading time: 0 Render date: 2024-05-16T05:35:01.027Z Has data issue: false hasContentIssue false

DEVELOPMENT OF A CLASSIFIER AND A SIMULATOR TO SUPPORT THE DESIGN OF AN ANTI-DECUBITUS ACTIVE MATTRESS

Published online by Cambridge University Press:  19 June 2023

Agnese Brunzini*
Affiliation:
Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Sciences
Marta Rossi
Affiliation:
Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Sciences
Marco Mandolini
Affiliation:
Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Sciences
Federica Cappelletti
Affiliation:
Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Sciences
Michele Germani
Affiliation:
Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Sciences
*
Brunzini, Agnese, Università Politecnica delle Marche, Italy, a.brunzini@pm.univpm.it

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Approximately 10% of hospitalized patients develops decubitus ulcers that quickly degenerates into chronic illness that reduces the quality of life and requires expensive clinical management. The use of an anti-decubitus active mattress, that automatically redistributes the pressure loads, reduces the occurrence of new lesions and promotes the healing of the pre-existing ones.

The aim of this work is to design and develop two tools to support the design of an anti-decubitus active mattress. Almost all the systems found in literature are based on the classification of pressure maps through machine learning and are difficultly usable in the design context.

This work proposes a pressure map Classifier and an Interactive Simulator of the mattress, based on a simpler logic, by integrating image processing techniques and functioning simulations. The Classifier can recognize the patient's pressure maps and classify them according to six reference sleep postures. The Interactive Simulator allows to understand the operating mechanisms of the mattress and to test the controller and the various control logics in the absence of a physical prototype.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Alwasel, A., Alossimi, B., Alsadun, M., Alhussaini, K. (2022), “Bedsores Management: Efficiency Simulation of a New Mattress Design”, Healthcare, 9, 12, https://dx.doi.org/10.3390/healthcare9121701Google Scholar
Beeckman, D., Serraes, B., Anrys, C., Van Tiggelen, H., Van Hecke, A.,Verhaeghe, S. (2019), “ A multicentre prospective randomised controlled clinical trial comparing the effectiveness and cost of a static air mattress and alternating air pressure mattress to prevent pressure ulcers in nursing home residents”, International Journal of Nursing Studies, 99, pp. 105113, https://dx.doi.org/10.1016/j.ijnurstu.2019.05.015CrossRefGoogle Scholar
Brunzini, A., Mandolini, M., Germani, M., Nester, C.J., Williams, A.E. (2018), “A Knowledge-Based and multi-user platform for prescribing custom-made insoles”, Proceedings of the DESIGN 2018 15th International Design Conference, pp. 25972608, https://doi.org/10.21278/idc.2018.0166CrossRefGoogle Scholar
Dempster, W. T. and Gaughran, G. R. (1967), “Properties of body segments based on size and weight”, The American Journal of Anatomy, Vol. 120, pp. 3354, https://dx.doi.org/10.1002/aja.1001200104.CrossRefGoogle Scholar
Islam, S.M.M., Lubecke, V.M. (2022), “Sleep Posture Recognition With a Dual-Frequency Microwave Doppler Radar and Machine Learning Classifiers”, IEEE Sensors Letters, 6,3, https://dx.doi.org/10.1109/LSENS.2022.3148378CrossRefGoogle Scholar
Hsiu-Chen, H. and Rong-Chin, L. (2013), “A New Mattress Development Based on Pressure Sensors for Body-contouring Uniform Support”, International Journal of Advanced Studies in Computer Science and Engineering, Vol. 2, Special Issue 1, pp. 15. https://doi.org/10.48550/arXiv.1308.2196Google Scholar
Huang, T.T.K., Aitken, J., Ferris, E., Cohen, N. (2018), “ Design thinking to improve implementation of public health interventions: an exploratory case study on enhancing park use”, Journal of Design for Health, Vol. 2 (2), pp. 236252, https://doi.org/10.1080/24735132.2018.1541047CrossRefGoogle ScholarPubMed
Jeng, P.Y., Wang, L.C., Hu, C.J., Wu, D. (2021), “ A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach”, Sensors, 21, 1, https://dx.doi.org/10.3390/s21010258CrossRefGoogle ScholarPubMed
Bai, Li, Liu, D., Chou, T.W., Hsu, H.L., Y.L. (2020), “ Relationship between a pressure redistributing foam mattress and pressure injuries: An observational prospective cohort study”, PLOS ONE, 15, 11, https://dx.doi.org/10.1371/journal.pone.0241276CrossRefGoogle ScholarPubMed
Liu, J.J., Xu, W., Huang, M.C., Alshurafa, N., Sarrafzadeh, M., Raut, N., Yadegar, B. (2013), “A Dense Pressure Sensitive Bedsheet Design for Unobtrusive Sleep Posture Monitoring”, IEEE International Conference on Pervasive Computing and Communications (PerCom), San Diego, CA, USA, 18-22 March 2013, IEEE, pp. 207215, DOI: 10.1109/PerCom.2013.6526734.CrossRefGoogle Scholar
Meaume, S., Marty, M. (2020), “ Pressure ulcer prevention using an alternating-pressure mattress overlay: the MATCARP project”, Journal of Wound Care, 29, 9, https://dx.doi.org/10.12968/jowc.2020.29.Sup9a.S32CrossRefGoogle Scholar
Rasouli, D.M.S., Payandeh, S. (2019), “ A novel depth image analysis for sleep posture estimation”, Journal of Ambient Intelligence and Humanized Computing, 10, 5, pp. 19992014, https://dx.doi.org/10.1007/s12652-018-0796-1CrossRefGoogle Scholar
Yousefi, R., Ostadabbas, S., Faezipour, M., Farshbaf, M. Nourani, M., Tamil, L., Pompeo, M. (2011) “Bed Posture Classification for Pressure Ulcer Prevention”, 33rd Annual International Conference of the IEEE EMBS, Boston, Massachusetts USA, August 30 - September 3 2011, IEEE, pp. 71757178, https://dx.doi.org/10.1109/IEMBS.2011.6091813.CrossRefGoogle Scholar
Yousefi, R., Ostadabbas, S., Faezipour, M., Nourani, M., Ng, V., Tamil, L., Bowling, A., Behan, D. and Pompeo, M. (2011), “A Smart Bed Platform for Monitoring & Ulcer Prevention”, 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China, 15-17 October 2011, IEEE, pp. 13621366, https://dx.doi.org/10.1109/BMEI.2011.6098589.Google Scholar