A Supervisory Control System for Flexible Hospital Rehabilitation Beds Based on Computer Vision
Authors: 
Bruno J. Santos, Tarcisio F. Leão, Rafael Palma, Daniel Martins, Eduardo G. P. BockAuthors Info & Claims
Bruno J. Santos, Tarcisio F. Leão, Rafael Palma, Daniel Martins, Eduardo G. P. BockAuthors Info & ClaimsPages 48 - 53
Abstract
This article investigates the potential of a computer vision algorithm to enhance patient care and optimize healthcare operations in rehabilitation hospital beds. The accuracy and reliability of the algorithm are critical factors that require careful consideration during the design and testing phases to ensure its effectiveness in detecting patient movements and behaviors. The objective of this study was to identify the contours of the patient and hospital bed through computer vision to detect and track patient movements and send commands to the bed control system to adjust the platform's position and inclination. The proposed computer vision algorithm was evaluated on a dataset of simulated patient images in hospital beds. The study successfully prepared the raw image data for analysis and classification using various image processing techniques. The results showed that the algorithm achieved an average precision of 80% in classifying a person, surpassing the practicality of methods associated with nurse monitoring at the bedside. Future work will focus on improving the artificial intelligence algorithm to detect patient activities and platform positions, ultimately enhancing patient care and safety in rehabilitation hospital beds.
References
[1]
Campos, A., Cortés, E., Martins, D., Ferre, M., & Contreras, A. (2021). Development of a flexible rehabilitation system for bedridden patients. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43(7), 361.
[2]
Barreto, R. L. P., Simoni, R., & Martins, D. (2018). An Initial Assessment of Mechanisms for the Development of New Hospital Beds. In Multibody Mechatronic Systems: Proceedings of the MUSME Conference held in Florianópolis, Brazil, October 24-28, 2017 6 (pp. 485-494). Springer International Publishing.
[3]
Vos, T., Lim, S. S., Abbafati, C., Abbas, K. M., Abbasi, M., Abbasifard, M., ... & Bhutta, Z. A. (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1204-1222.
[4]
Cieza, A., Causey, K., Kamenov, K., Hanson, S. W., Chatterji, S., & Vos, T. (2020). Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10267), 2006-2017.
[5]
Tortorella, G. L., Fogliatto, F. S., Mac Cawley Vergara, A., Vassolo, R., & Sawhney, R. (2020). Healthcare 4.0: trends, challenges and research directions. Production Planning & Control, 31(15), 1245-1260.
[6]
Mosca, R., Mosca, M., Revetria, R., Currò, F., & Briatore, F. (2023). Advanced 4.0 Bed Management System.
[7]
Yonezawa, Y., Miyamoto, Y., Maki, H., Ogawa, H., Ninomiya, I., Sada, K., ... & Caldwell, W. M. (2005). A new intelligent bed care system for hospital and home patients. Biomedical instrumentation & technology, 39(4), 313-319.
[8]
Vázquez-Santacruz, E., Cruz-Santos, W., & Gamboa-Zúñiga, M. (2015). Design and implementation of an intelligent system for controlling a robotic hospital bed for patient care assistance. Computación y Sistemas, 19(3), 467-474.
[9]
Yeung, S., Downing, N. L., Fei-Fei, L., & Milstein, A. (2018). Bedside computer vision-moving artificial intelligence from driver assistance to patient safety. N Engl J Med, 378(14), 1271-1273.
[10]
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ... & Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ digital medicine, 4(1), 5.
[11]
Blair, J., Corrigall, H., Angus, N. J., Thompson, D. R., & Leslie, S. (2011). Home versus hospital-based cardiac rehabilitation: a systematic review. Rural and Remote Health, 11(2), 190-206.
[12]
Gelaw, A. Y., Janakiraman, B., Gebremeskel, B. F., & Ravichandran, H. (2020). Effectiveness of Home-based rehabilitation in improving physical function of persons with Stroke and other physical disability: A systematic review of randomized controlled trials. Journal of Stroke and Cerebrovascular Diseases, 29(6), 104800.
[13]
Miri, F., Jahanmehr, N., & Goudarzi, R. (2021). Evaluating the Cost-effectiveness Analysis of Rehabilitation Methods for Patients with Stroke.
[14]
Santos, B., Martins, D., Leao, T., & Bock, E. (2021, November). Supervisory control system for hospital rehabilitation beds. In 2021 9th International Conference on Control, Mechatronics and Automation (ICCMA) (pp. 130-134). IEEE.
[15]
OpenCV. "OpenCV Documentation 4.0.1". Accessed April 10, 2023. Available at: https://docs.opencv.org/4.x/.
[16]
Liu, S., Huang, X., Fu, N., Li, C., Su, Z., & Ostadabbas, S. (2022). Simultaneously-collected multimodal lying pose dataset: Enabling in-bed human pose monitoring. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 1106-1118.
[17]
OpenPose. "OpenPose Documentation 1.7". Accessed April 10, 2023. Available at: https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_00_index.html.
[18]
ImageAI. "ImageAI Documentation 3.0.3". Accessed April 10, 2023. Available at: https://imageai.readthedocs.io/en/latest/.
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Published In
July 2023
90 pages
ISBN:9798400707742
DOI:10.1145/3608143
Copyright © 2023 ACM.
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Published: 07 August 2023
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RCVE 2023
RCVE 2023: 2023 International Conference on Robotics, Control and Vision Engineering
July 21 - 23, 2023
Tokyo, Japan
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