Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode_Journal of Biomedical Engineering

Authors：

LI Lixuan ¹ , LIANG Hong ¹ , FAN Yong ¹ , YAN Wei ² , YAN Muyang ² ,  CAO Desen ³ , ZHANG Zhengbo ¹

1. Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing 100853, P. R. China;
2. Department of Hyperbaric Oxygen, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, P. R. China;
3. Department of Biomedical Engineering, Medical Security Center, Chinese PLA General Hospital, Beijing 100853, P. R. China;

Corresponding author：

CAO Desen, Email: caodesen2012@126.com

Keywords：

Wearable physiological monitoring; Ward Internet of Things; In-patient monitoring; Digital cardiopulmonary rehabilitation; In- and out-patient connected monitoring

DOI：

10.7507/1001-5515.202211047

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Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients’ cardiopulmonary function, and management of patients outside hospital.

Citation： LI Lixuan, LIANG Hong, FAN Yong, YAN Wei, YAN Muyang, CAO Desen, ZHANG Zhengbo. Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode. Journal of Biomedical Engineering, 2023, 40(6): 1053-1061. doi: 10.7507/1001-5515.202211047 Copy

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27.	Bozkurt B, Fonarow G C, Goldberg L R, et al. Cardiac rehabilitation for patients with heart failure: JACC expert panel. J Am Coll Cardiol, 2021, 77(11): 1454-1469.
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1. Ates H C, Nguyen P Q, Gonzalez-Macia L, et al. End-to-end design of wearable sensors. Nat Rev Mater, 2022, 7: 1-21.
2. Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol, 2021, 18(8): 581-599.
3. Iqbal S, Mahgoub I, Du E, et al. Advances in healthcare wearable devices. NPJ Flex Electron, 2021, 5(1): 1-14.
4. Mishra T, Wang M, Metwally A A, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng, 2020, 4(12): 1208-1220.
5. Quer G, Radin J M, Gadaleta M, et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med, 2021, 27(1): 73-77.
6. Perez M V, Mahaffey K W, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. New Engl J Med, 2019, 381(20): 1909-1917.
7. Loncar-Turukalo T, Zdravevski E, da Silva J M, et al. Literature on wearable technology for connected health: scoping review of research trends, advances, and barriers. J Med Internet Res, 2019, 21(9): e14017.
8. MacKinnon G E, Brittain E L. Mobile health technologies in cardiopulmonary disease. Chest, 2020, 157(3): 654-664.
9. 张政波, 俞梦孙, 李若新, 等. 背心式呼吸感应体积描记系统设计. 航天医学与医学工程, 2006, 19(5): 5.
10. Zhang Z-B, Shen Y-H, Wang W-D, et al. Design and implementation of sensing shirt for ambulatory cardiopulmonary monitoring. J Med Biol Eng, 2011, 31(3): 207-215.
11. 曹德森, 李德玉, 张政波, 等. 随行生理监护系统设计及性能初步验证. 生物医学工程学杂志, 2019, 36(1): 121-130.
12. Shen Y H, Li C M. Design and implementation of a wearable multiparameter physiological monitoring system for the study of human heat stress, cold stress, and thermal comfort. Instrum Sci Technol, 2012, 40(4): 290-304.
13. Zhang Z, Zheng J, Wu H, et al. Development of a respiratory inductive plethysmography module supporting multiple sensors for wearable systems. Sensors, 2012, 12(10): 13167-13184.
14. Xu H, Yan W, Lan K, et al. Assessing electrocardiogram and respiratory signal quality of a wearable device (sensecho): semisupervised machine learning-based validation study. JMIR mHealth uHealth, 2021, 9(8): e25415.
15. Liu X, Liu T, Zhang Z, et al. TOP-Net prediction model using bidirectional long short-term memory and medical-grade wearable multisensor system for tachycardia onset: algorithm development study. JMIR Med Inf, 2021, 9(4): e18803.
16. Ma C, Lan K, Wang J, et al. Arrhythmia detection based on multi-scale fusion of hybrid deep models from single lead ECG recordings: A multicenter dataset study. Biomed Signal Proces, 2022, 77: 103753.
17. Schein R M, Hazday N, Pena M, et al. Clinical antecedents to in-hospital cardiopulmonary arrest. Chest, 1990, 98(6): 1388-1392.
18. 王钊, 梁洪, 王佳晨, 等. 基于可穿戴技术的临床监护新模式探索研究. 生物医学工程学杂志, 2021, 38(4): 753-763.
19. ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med, 2002, 166: 111-117.
20. Wang J, Zang Y, Wu Q, et al. Predicting adverse events during six-minute walk test using continuous physiological signals. Front Physiol, 2022, 13: 887954.
21. 藏雅宁. 基于数字化六分钟步行试验的连续生理数据预测价值挖掘研究. 上海: 上海体育学院, 2023.
22. Holland A E, Hill C J, Jones A Y, et al. Breathing exercises for chronic obstructive pulmonary disease. Cochrane Database Syst Rev, 2012, 10: CD008250.
23. Yau K K-Y, Loke A Y. Effects of diaphragmatic deep breathing exercises on prehypertensive or hypertensive adults: A literature review. Complement Ther Clin, 2021, 43: 101315.
24. Jafari H, Gholamrezaei A, Franssen M, et al. Can slow deep breathing reduce pain? An experimental study exploring mechanisms. J Pain, 2020, 21(9-10): 1018-1030.
25. Laborde S, Hosang T, Mosley E, et al. Influence of a 30-day slow-paced breathing intervention compared to social media use on subjective sleep quality and cardiac vagal activity. J Clin Med, 2019, 8(2): 193.
26. Li X, Yu R, Wang P, et al. Effects of exercise training on cardiopulmonary function and quality of life in elderly patients with pulmonary fibrosis: A meta-analysis. Int J Env Res Public Health, 2021, 18(14): 7643.
27. Bozkurt B, Fonarow G C, Goldberg L R, et al. Cardiac rehabilitation for patients with heart failure: JACC expert panel. J Am Coll Cardiol, 2021, 77(11): 1454-1469.
28. Althoff T, Sosič R, Hicks J L, et al. Large-scale physical activity data reveal worldwide activity inequality. Nature, 2017, 547(7663): 336-339.
29. McConnell M V, Shcherbina A, Pavlovic A, et al. Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart Counts Cardiovascular Health Study. JAMA Cardiol, 2017, 2(1): 67-76.
30. Schüssler-Fiorenza Rose S M, Contrepois K, Moneghetti K J, et al. A longitudinal big data approach for precision health. Nat Med, 2019, 25(5): 792-804.

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