Bedridden for 6 Months Walking Again Cancer
Posture Monitoring for Wellness Intendance of Crippled Elderly Patients Using 3D Human Skeleton Analysis via Machine Learning Arroyo
by one,2,iii , 1,two,3,* 
, 4,* , 1 , ane , one and 1
1
Department of Electrical Engineering, National Chung Cheng University (CCU), Chiayi 62102, Taiwan
2
Center for Innovative Research on Crumbling Society (CIRAS), National Chung Cheng Academy (CCU), Chiayi 62102, Taiwan
3
Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI), National Chung Cheng University (CCU), Chiayi 62102, Taiwan
4
Rehabilitation Section, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi 60002, Taiwan
*
Authors to whom correspondence should be addressed.
Academic Editor: Simone Morais
Appl. Sci. 2022, 12(6), 3087; https://doi.org/10.3390/app12063087 (registering DOI)
Received: 19 January 2022 / Revised: 28 February 2022 / Accustomed: 15 March 2022 / Published: 17 March 2022
Abstract
For bedridden elderly people, pressure ulcer is the most common and serious complexity and could be prevented past regular repositioning. Nonetheless, due to a shortage of long-term care workers, repositioning might non exist implemented equally oftentimes as required. Posture monitoring by using mod wellness/medical caring applied science can potentially solve this problem. We propose a RGB-D camera system to recognize the posture of the bedridden elderly patients based on the analysis of 3D human skeleton which consists of articulated joints. Since practically most bedridden patients were covered with a blanket, merely four 3D joints were used in our system. Afterwards the recognition of the posture, a alarm message will be sent to the caregiver for help if the patient stays in the same posture for more a predetermined period (e.g., two hours). Experimental results betoken that our proposed method is capable of achieving a high accuracy in posture recognition (in a higher place 95%). To the best of our knowledge, this application of using human skeleton analysis for patient care is novel. The proposed scheme is promising for clinical applications and will undertake an intensive examination in health care facilities in the near future after redesigning a proper RGB-D (Red-Green-Bluish-Depth) camera system. In addition, a desktop computer tin can be used for multi-point monitoring to reduce cost, since existent-time processing is not required in this awarding. View Full-Text
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MDPI and ACS Style
Chiang, J.-C.; Prevarication, Due west.-N.; Huang, H.-C.; Chen, K.-T.; Liang, J.-Y.; Lo, Y.-C.; Huang, Westward.-H. Posture Monitoring for Wellness Care of Crippled Elderly Patients Using 3D Homo Skeleton Assay via Machine Learning Approach. Appl. Sci. 2022, 12, 3087. https://doi.org/10.3390/app12063087
AMA Style
Chiang J-C, Lie W-Northward, Huang H-C, Chen 1000-T, Liang J-Y, Lo Y-C, Huang Due west-H. Posture Monitoring for Health Care of Crippled Elderly Patients Using 3D Human Skeleton Assay via Machine Learning Approach. Applied Sciences. 2022; 12(6):3087. https://doi.org/10.3390/app12063087
Chicago/Turabian Style
Chiang, Jui-Chiu, Wen-Nung Lie, Hsiu-Chen Huang, Kuan-Ting Chen, Jhih-Yuan Liang, Yu-Chia Lo, and Wei-Hao Huang. 2022. "Posture Monitoring for Health Care of Bedridden Elderly Patients Using 3D Human being Skeleton Analysis via Machine Learning Approach" Practical Sciences 12, no. 6: 3087. https://doi.org/ten.3390/app12063087
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Source: https://www.mdpi.com/2076-3417/12/6/3087
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