Wearable devices: Perspectives on assessing and monitoring human physiological status_Journal of Biomedical Engineering

Authors：

 PENG Chung-Kang ^1,2,3 , CUI Xingran ^1,2 , ZHANG Zhengbo ⁴ , YU Mengsun ⁵

1. School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China;
2. Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, P. R. China;
3. Beth Israel Deaconess Medical Center / Harvard Medical School, Boston 02215, USA;
4. Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing 100853, P. R. China;
5. Aviation Medical Engineering Research Center, Air Force Medical Center, Beijing 100142, P. R. China;

Corresponding author：

PENG Chung-Kang, Email: ckpeng@seu.edu.cn

Keywords：

Wearable devices; Medical and health care; Dynamical complexity

DOI：

10.7507/1001-5515.202303043

Video：

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Abstract Full text Figures/Tables Video References Cited by

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis—dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.

Citation： PENG Chung-Kang, CUI Xingran, ZHANG Zhengbo, YU Mengsun. Wearable devices: Perspectives on assessing and monitoring human physiological status. Journal of Biomedical Engineering, 2023, 40(6): 1045-1052. doi: 10.7507/1001-5515.202303043 Copy

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1. Himmelstein D U, Lawless R M, Thorne D, et al. Medical bankruptcy: still common despite the Affordable Care Act. Am J Public Health, 2019, 109(3): 431-433.
2. Holman H R. The relation of the chronic disease epidemic to the health care crisis. ACR Open Rheumatol, 2020, 2(3): 167-173.
3. Perez M V, Mahaffey K W, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
4. Kim D, Choi Y. Applications of smart glasses in applied sciences: A systematic review. Appl Sci, 2021, 11(11): 4956.
5. Poongodi M, Hamdi M, Malviya M, et al. Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods. Pers Ubiquitous Comput, 2022, 26(1): 25-35.
6. Ozioko O, Dahiya R. Smart tactile gloves for haptic interaction, communication, and rehabilitation. Adv Intell Syst, 2022, 4(2): 2100091.
7. Ilyas S M, Jawed S F, Shakeel C S, et al. Design and development of a stroke rehabilitation glove for measuring and monitoring hand motions. Pak J Rehabil, 2022, 11(2): 177-188.
8. Acar G, Ozturk O, Yapici M K. Wearable graphene nanotextile embedded smart armband for cardiac monitoring// 2018 IEEE SENSORS. New Delhi: IEEE, 2018: 1-4.
9. Zhang S, Zhao Y, Nguyen D T, et al. Necksense: A multi-sensor necklace for detecting eating activities in free-living conditions. Proc ACM Interact Mob Wearable Ubiquitous Technol, 2020, 4(2): 1-26.
10. Chen T, Li Y, Tao S, et al. Neckface: Continuously tracking full facial expressions on neck-mounted wearables. Proc ACM Interact Mob Wearable Ubiquitous Technol, 2021, 5(2): 1-31.
11. Stojanović S, Geršak J, Uran S. Development of the smart T-shirt for monitoring thermal status of athletes. Autex Res J, 2022, 23(2): 266-278.
12. 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.
13. 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.
14. Jeong K, Lee K C. Artificial neural network-based abnormal gait pattern classification using smart shoes with a gyro sensor. Electronics, 2022, 11(21): 3614.
15. Zhao H, Wang R, Qi D, et al. Wearable gait monitoring for diagnosis of neurodegenerative diseases. Measurement, 2022, 202: 111839.
16. Lou Z, Wang L, Jiang K, et al. Reviews of wearable healthcare systems: Materials, devices and system integration. Mater Sci Eng R-Rep, 2020, 140: 100523.
17. Ates H C, Nguyen P Q, Gonzalez-Macia L, et al. End-to-end design of wearable sensors. Nat Rev Mater, 2022, 7(11): 887-907.
18. Zhang S, Li Y, Zhang S, et al. Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors, 2022, 22(4): 1476.
19. 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.
20. Samol A, Bischof K, Luani B, et al. Single-lead ECG recordings including Einthoven and Wilson leads by a smartwatch: a new era of patient directed early ECG differential diagnosis of cardiac diseases?. Sensors, 2019, 19(20): 4377.
21. Nelson B W, Allen N B. Accuracy of consumer wearable heart rate measurement during an ecologically valid 24-hour period: intraindividual validation study. JMIR mHealth uHealth, 2019, 7(3): e10828.
22. Kario K, Shimbo D, Tomitani N, et al. The first study comparing a wearable watch-type blood pressure monitor with a conventional ambulatory blood pressure monitor on in-office and out-of-office settings. J Clin Hypertens, 2020, 22(2): 135-141.
23. Bard D M, Joseph J I, van Helmond N. Cuff-less methods for blood pressure telemonitoring. Front Cardiovasc Med, 2019, 6: 40.
24. Iqbal S M A, Mahgoub I, Du E, et al. Advances in healthcare wearable devices. NPJ Flex Electron, 2021, 5(1): 9.
25. Kim J, Campbell A S, de Ávila B E F, et al. Wearable biosensors for healthcare monitoring. Nat Biotechnol, 2019, 37(4): 389-406.
26. Mughal H, Javed A R, Rizwan M, et al. Parkinson’s disease management via wearable sensors: a systematic review. IEEE Access, 2022, 10: 35219-35237.
27. Mohammadian Rad N, Van Laarhoven T, Furlanello C, et al. Novelty detection using deep normative modeling for imu-based abnormal movement monitoring in Parkinson’s disease and autism spectrum disorders. Sensors, 2018, 18(10): 3533.
28. Ma C, Li D, Pan L, et al. Quantitative assessment of essential tremor based on machine learning methods using wearable device. Biomed Signal Process Control, 2022, 71: 103244.
29. Ma C, Zhang P, Wang J, et al. Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study. Comput Meth Programs Biomed, 2022, 219: 106741.
30. 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 Process Control, 2022, 77: 103753.
31. Gao Y, Long Y, Guan Y, et al. Towards reliable, automated general movement assessment for perinatal stroke screening in infants using wearable accelerometers. Proc ACM Interact Mob Wearable Ubiquitous Technol, 2019, 3(1): 1-22.
32. Parajuli N, Sreenivasan N, Bifulco P, et al. Real-time EMG based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation. Sensors, 2019, 19(20): 4596.
33. Elfaramawy T, Fall C L, Arab S, et al. A wireless respiratory monitoring system using a wearable patch sensor network. IEEE Sens J, 2018, 19(2): 650-657.
34. Moghimi M J, Bradley C, Maire N, et al. Multimodal wearable platform for remote monitoring of breathing patterns, cough events and blood oxygen level// Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables III. SPIE, 2022, 11956: 100-104.
35. Chen G, Shen S, Tat T, et al. Wearable respiratory sensors for COVID-19 monitoring. View, 2022, 3(5): 20220024.
36. Kennel P J, Rosenblum H, Axsom K M, et al. Remote cardiac monitoring in patients with heart failure: a review. JAMA Cardiol, 2022, 7(5): 556-564.
37. Gautam N, Ghanta S N, Mueller J, et al. Artificial intelligence, wearables and remote monitoring for heart failure: Current and future applications. Diagnostics, 2022, 12(12): 2964.
38. Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization: the LINK-HF multicenter study. Circ-Heart Fail, 2020, 13(3): e006513.
39. Anand I S, Greenberg B H, Fogoros R N, et al. Design of the Multi-Sensor Monitoring in Congestive Heart Failure (MUSIC) study: prospective trial to assess the utility of continuous wireless physiologic monitoring in heart failure. J Card Fail, 2011, 17(1): 11-16.
40. Anand I S, Tang W H W, Greenberg B H, et al. Design and performance of a multisensor heart failure monitoring algorithm: results from the multisensor monitoring in congestive heart failure (MUSIC) study. J Card Fail, 2012, 18(4): 289-295.
41. Davies H J, Bachtiger P, Williams I, et al. Wearable in-ear PPG: Detailed respiratory variations enable classification of COPD. IEEE Trans Biomed Eng, 2022, 69(7): 2390-2400.
42. Tiwari A, Liaqat S, Liaqat D, et al. Remote COPD severity and exacerbation detection using heart rate and activity data measured from a wearable device// 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Mexico: IEEE, 2021: 7450-7454.
43. Wu C T, Li G H, Huang C T, et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study. JMIR mHealth uHealth, 2021, 9(5): e22591.
44. Zhu G, Li J, Meng Z, et al. Learning from large-scale wearable device data for predicting epidemics trend of COVID-19. Discrete Dyn Nat Soc, 2020, 2020: 1-8.
45. Seshadri D R, Davies E V, Harlow E R, et al. Wearable sensors for COVID-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Front Digit Health, 2020, 2: 8.
46. 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.
47. Hussain S A, Al Bassam N, Zayegh A, et al. Prediction and evaluation of healthy and unhealthy status of COVID-19 patients using wearable device prototype data. MethodsX, 2022, 9: 101618.
48. Peng C K, Costa M, Goldberger A L. Adaptive data analysis of complex fluctuations in physiologic time series. Adv Adapt Data Anal, 2009, 1(1): 61-70.
49. Goldberger A L, Peng C K, Lipsitz L A. What is physiologic complexity and how does it change with aging and disease?. Neurobiol Aging, 2002, 23(1): 23-26.
50. Goldberger A L, Amaral L A N, Hausdorff J M, et al. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci, 2002, 99(suppl_1): 2466-2472.
51. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett, 2002, 89(6): 068102.
52. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of biological signals. Phys Rev E, 2005, 71(2): 021906.
53. Hsu C F, Wei S Y, Huang H P, et al. Entropy of entropy: Measurement of dynamical complexity for biological systems. Entropy, 2017, 19(10): 550.
54. Martyn C. Complexity and Healthcare: An Introduction. BMJ, 2003, 326(7382): 228.
55. Bar-Yam Y. General features of complex systems. UNESCO, Oxford: EOLSS Publishers, 2002, 1.
56. Strogatz S H. Nonlinear dynamics and chaos with student solutions manual: With applications to physics, biology, chemistry, and engineering. Boca Raton: CRC press, 2018.
57. Smith G B, Prytherch D R, Meredith P, et al. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation, 2013, 84(4): 465-470.
58. Silcock D J, Corfield A R, Gowens P A, et al. Validation of the National Early Warning Score in the prehospital setting. Resuscitation, 2015, 89: 31-35.
59. Spångfors M, Bunkenborg G, Molt M, et al. The National Early Warning Score predicts mortality in hospital ward patients with deviating vital signs: A retrospective medical record review study. J Clin Nurs, 2019, 28(7-8): 1216-1222.
60. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 1996, 93(5): 1043-1065.
61. Sassi R, Cerutti S, Lombardi F, et al. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace, 2015, 17(9): 1341-1353.
62. Cui X, Chang E, Yang W H, et al. Automated detection of paroxysmal atrial fibrillation using an information-based similarity approach. Entropy, 2017, 19(12): 677.
63. Han D, Bashar S K, Mohagheghian F, et al. Premature atrial and ventricular contraction detection using photoplethysmographic data from a smartwatch. Sensors, 2020, 20(19): 5683.
64. Benichou T, Pereira B, Mermillod M, et al. Heart rate variability in type 2 diabetes mellitus: A systematic review and meta–analysis. PLoS One, 2018, 13(4): e0195166.
65. Lutfi M F, Sukkar M Y. Effect of blood pressure on heart rate variability. Khartoum Med J, 2012, 4(1): 548-553.
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