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Title: A Device for Reducing Pressure Ulcers in Bedridden Patients Using Fiber Reinforced Elastomeric Enclosures (FREEs)
Abstract

This paper describes the design, development, and prototype testing of a device that can relieve contact pressure to potentially prevent pressure ulcers in bedridden patients by utilizing pneumatically actuated Fiber-Reinforced Elastomeric Enclosures (FREEs) [1,2].

Bedsores, or pressure ulcers, develop in bedridden patients due to constant contact pressure between a patient’s skin and an external (bed) surface. It is estimated that over 2.5 million patients [8] suffer from pressure ulcer developments annually in the United States. High pressure areas of the body include the sacrum and heel, with 36% of pressure ulcers occurring at the sacrum and 30% occurring at the heel. All other body areas each account for only 6% of pressure ulcer occurrence [6]. They are a major concern for low-mobility, patients who are bedridden for an extended periods and are associated with a 5x increase in patient mortality [3]. In addition, pressure ulcers place a significant cost burden on patients. Development of Stage 4 pressure ulcers and associated comorbidities can cost on average $127,000 to the patient [4]. This high cost is mainly due to attending caregiver’s time.

The most common solution for preventing the development of pressure ulcers in bed ridden hospital patients is for the attending nurses to reposition the patients every 2 hours so that the affected areas are relieved of any contact pressure. Repositioning patients is time consuming and strenuous for health care providers. General purpose “dynamic” pressure relief mattresses have shown to be somewhat effective in reducing the development of pressure ulcers. However, they do not effectively target high pressure areas and still necessitate frequent repositioning.

FREEs can be designed to generate a variety of shapes and motions once actuated (pressurized) and serve as building blocks for soft robotic applications. When FREEs, arranged in parallel, are embedded in a material with compatible elastic properties they propagate their deformed shape throughout the surface. These compliant sheets of FREEs are capable of sustaining loads while relieving pressure on high pressure areas of the body. The prototype for the device presented in this paper was designed for relieving pressure on a patient’s heel area. Preliminary test results demonstrate that the prototype device is effective at lifting a patient’s ankle, for patients weighing up to 250lbs, thus relieving contact pressure. The research also demonstrates the viability of developing modular pressure-relieving pads embedded with more advanced FREEs than described in this paper that can be tailored to relieve contact pressure on other affected areas such as the sacrum.

 
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Award ID(s):
1830950
NSF-PAR ID:
10501599
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
American Society of Mechanical Engineers
Date Published:
Journal Name:
Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition. Volume 4: Biomedical and Biotechnology; Design, Systems, and Complexity.
Volume:
4
Page Range / eLocation ID:
V004T05A006.
Format(s):
Medium: X
Location:
Columbus, Ohio, USA
Sponsoring Org:
National Science Foundation
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The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201. [5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021]. [6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/. [7] J. Picone, T. Farkas, I. Obeid, and Y. 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