Research progress on wearable physiological parameter monitoring and its clinical applications_Journal of Biomedical Engineering

Authors：

MA Chenbin ^1,2 , XU Haoran ³ , LI Deyu ² ,  ZHANG Zhengbo ¹

1. Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing 100853, P.R.China;
2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P.R.China;
3. Medical School of Chinese PLA, Beijing 100853, P.R.China;

Corresponding author：

Keywords：

wearable technology; physiological monitoring; clinical application research; medical-grade; clinical application mode

DOI：

10.7507/1001-5515.202009031

Video：

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Abstract Full text Figures/Tables Video References Cited by

Wearable physiological parameter monitoring devices play an increasingly important role in daily health monitoring and disease diagnosis/treatment due to their continuous dynamic and low physiological/psychological load characteristics. After decades of development, wearable technologies have gradually matured, and research has expanded to clinical applications. This paper reviews the research progress of wearable physiological parameter monitoring technology and its clinical applications. Firstly, it introduces wearable physiological monitoring technology’s research progress in terms of sensing technology and data processing and analysis. Then, it analyzes the monitoring physiological parameters and principles of current medical-grade wearable devices and proposes three specific directions of clinical application research: 1) real-time monitoring and predictive warning, 2) disease assessment and differential diagnosis, and 3) rehabilitation training and precision medicine. Finally, the challenges and response strategies of wearable physiological monitoring technology in the biomedical field are discussed, highlighting its clinical application value and clinical application mode to provide helpful reference information for the research of wearable technology-related fields.

Citation： MA Chenbin, XU Haoran, LI Deyu, ZHANG Zhengbo. Research progress on wearable physiological parameter monitoring and its clinical applications. Journal of Biomedical Engineering, 2021, 38(3): 583-593. doi: 10.7507/1001-5515.202009031 Copy

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1. Pantelopoulos A, Bourbakis N G. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern C Appl Rev, 2010, 40(1): 1-12.
2. Heikenfeld J, Jajack A, Rogers J, et al. Wearable sensors: modalities, challenges, and prospects. Lab Chip, 2018, 18(2): 217-248.
3. O’Donnell J, Velardo C, Shah S A, et al. Physical activity and sleep analysis of heart failure patients using multi-sensor patches// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 6092-6095.
4. Verrillo S C, Cvach M, Hudson K W, et al. Using continuous vital sign monitoring to detect early deterioration in adult postoperative inpatients. J Nurs Care Qual, 2019, 34(2): 107-113.
5. Paul J E, Chong M A, Buckley N, et al. Vital sign monitoring with continuous pulse oximetry and wireless clinical notification after surgery (the VIGILANCE pilot study)—a randomized controlled pilot trial. Pilot Feasibility Stud, 2019, 5(1): 1-8.
6. Razjouyan J, Grewal G S, Rishel C, et al. Activity monitoring and heart rate variability as indicators of fall risk: proof-of-concept for application of wearable sensors in the acute care setting. J Gerontol Nurs, 2017, 43(7): 53-62.
7. Breteler M J M, Kleinjan E J, Dohmen D A J, et al. Vital signs monitoring with wearable sensors in high-risk surgical patients: a clinical validation study. Anesthesiology, 2020, 132(3): 424-439.
8. Herrnstadt G, McKeown M J, Menon C. Controlling a motorized orthosis to follow elbow volitional movement: tests with individuals with pathological tremor. J Neuroeng Rehabil, 2019, 16(1): 1-14.
9. Galli A, Ambrosini F, Lombardi F. Holter monitoring and loop recorders: from research to clinical practice. Arrhythm Electrophysiol Rev, 2016, 5(2): 136-143.
10. Kelleher J F. Pulse oximetry. J Clin Monit, 1989, 5(1): 37-62.
11. Wang T W, Lin S F. Wearable piezoelectric-based system for continuous beat-to-beat blood pressure measurement. Sensors (Basel), 2020, 20(3): 851.
12. Houssein A, Ge D, Gastinger S, et al. Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods. Physiol Meas, 2019, 40(3): 03TR01.
13. Rykov Y, Thach T Q, Dunleavy G, et al. Activity tracker-based metrics as digital markers of cardiometabolic health in working adults: cross-sectional study. JMIR Mhealth Uhealth, 2020, 8(1): e16409.
14. 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.
15. Li P, Yang Z, Yan W, et al. MobiCardio: a clinical-grade mobile health system for cardiovascular disease management// 2019 IEEE International Conference on Healthcare Informatics (ICHI). Xi'an: IEEE, 2019: 1-6.
16. 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.
17. Zhuo K, Gao C, Wang X, et al. Stress and sleep: a survey based on wearable sleep trackers among medical and nursing staff in Wuhan during the COVID-19 pandemic. Gen Psychiatr, 2020, 33(3): e100260.
18. Goud K Y, Moonla C, Mishra R K, et al. Wearable electrochemical microneedle sensor for continuous monitoring of levodopa: toward Parkinson management. ACS Sens, 2019, 4(8): 2196-2204.
19. Yun I, Jeung J, Kim M, et al. Ultra-low power wearable infant sleep position sensor. Sensors (Basel), 2019, 20(1): 61.
20. Ma D, Wu X, Wang Y, et al. Wearable, antifreezing, and healable epidermal sensor assembled from long-lasting moist conductive nanocomposite organohydrogel. ACS Appl Mater Interfaces, 2019, 11(44): 41701-41709.
21. Di Rienzo M, Rizzo G, Isilay Z M, et al. SeisMote: a multi-sensor wireless platform for cardiovascular monitoring in laboratory, daily life, and telemedicine. Sensors (Basel), 2020, 20(3): 680.
22. Hershman S G, Bot B M, Shcherbina A, et al. Physical activity, sleep and cardiovascular health data for 50 000 individuals from the MyHeart Counts Study. Sci Data, 2019, 6(1): 1-10.
23. Ballinger B, Hsieh J, Singh A, et al. DeepHeart: semi-supervised sequence learning for cardiovascular risk prediction// Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Louisiana: AAAI, 2018, 32(1): 2079-2086.
24. Xu X, Deng J, Cummins N, et al. Autonomous emotion learning in speech: a view of zero-shot speech emotion recognition// Proc Interspeech 2019. Graz: ISCA, 2019: 949-953.
25. Regalia G, Onorati F, Lai M, et al. Multimodal wrist-worn devices for seizure detection and advancing research: focus on the Empatica wristbands. Epilepsy Res, 2019, 153: 79-82.
26. Isaacson S, Pahwa R, Pappert E, et al. Detecting bradykinesia in early morning off: a large database study using the Personal KinetiGraph® (PKG®). Neurology, 2020, 94(15 Supplement): 4125.
27. Patlar Akbulut F, Ikitimur B, Akan A. Wearable sensor-based evaluation of psychosocial stress in patients with metabolic syndrome. Artif Intell Med, 2020, 104: 101824.
28. Batista D, Placido Da Silva H, Fred A, et al. Benchmarking of the BITalino biomedical toolkit against an established gold standard. Healthc Technol Lett, 2019, 6(2): 32-36.
29. Jayarathna T, Gargiulo G D, Breen P P. Continuous vital monitoring during sleep and light activity using carbon-black elastomer sensors. Sensors (Basel), 2020, 20(6): 1583.
30. Nazari G, Macdermid J C. Minimal detectable change thresholds and responsiveness of Zephyr bioharness and Fitbit charge devices. J Strength Cond Res, 2020, 34(1): 257-263.
31. Frerichs I, Vogt B, Wacker J, et al. Multimodal remote chest monitoring system with wearable sensors: a validation study in healthy subjects. Physiol Meas, 2020, 41(1): 015006.
32. Seshadri D R, Li R T, Voos J E, et al. Wearable sensors for monitoring the internal and external workload of the athlete. NPJ Digit Med, 2019, 2(1): 1-18.
33. Nakamura T, Alqurashi Y D, Morrell M J, et al. Hearables: automatic overnight sleep monitoring with standardized in-ear EEG sensor. IEEE Trans Biomed Eng, 2020, 67(1): 203-212.
34. Vandecasteele K, De Cooman T, Dan J, et al. Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels. Epilepsia, 2020, 61(4): 766-775.
35. Izmailova E S, McLean I L, Hather G, et al. Continuous monitoring using a wearable device detects activity-induced heart rate changes after administration of amphetamine. Clin Transl Sci, 2019, 12(6): 677-686.
36. Buekers J, Theunis J, De Boever P, et al. Wearable finger pulse oximetry for continuous oxygen saturation measurements during daily home routines of patients with chronic obstructive pulmonary disease (COPD) over one week: observational study. JMIR Mhealth Uhealth, 2019, 7(6): e12866.
37. Mangiarotti M, Ferrise F, Graziosi S, et al. A wearable device to detect in real-time bimanual gestures of basketball players during training sessions. J Comput Inf Sci Eng, 2019, 19(1): 011004.
38. Sana F, Isselbacher E M, Singh J P, et al. Wearable devices for ambulatory cardiac monitoring: JACC state-of-the-art review. J Am Coll Cardiol, 2020, 75(13): 1582-1592.
39. Fouassier D, Roy X, Blanchard A, et al. Assessment of signal quality measured with a smart 12-lead ECG acquisition T-shirt. Ann Noninvasive Electrocardiol, 2020, 25(1): e12682.
40. Page R A. Technology-enabled seizure detection and reporting: the epilepsy network project. Epilepsy Res, 2019, 153: 85-87.
41. Chung H U, Rwei A Y, Hourlier-Fargette A, et al. Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units. Nat Med, 2020, 26(3): 418-429.
42. Lee K, Ni X, Lee J Y, et al. Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch. Nat Biomed Eng, 2020, 4(2): 148-158.
43. Tang X, Ma Z, Hu Q, et al. A real-time arrhythmia heartbeats classification algorithm using parallel delta modulations and rotated linear-kernel support vector machines. IEEE Trans Biomed Eng, 2020, 67(4): 978-986.
44. Fan K G, Mandel J, Agnihotri P, et al. Remote patient monitoring technologies for predicting chronic obstructive pulmonary disease exacerbations: review and comparison. JMIR Mhealth Uhealth, 2020, 8(5): e16147.
45. Frechette M L, Meyer B M, Tulipani L J, et al. Next steps in wearable technology and community ambulation in multiple sclerosis. Curr Neurol Neurosci Rep, 2019, 19(10): 1-10.
46. 李攀, 蒋敏, 向超, 等. 基于多传感器的可穿戴式足底压力测试系统研发及临床应用. 中国医学装备, 2019(3): 1-5.
47. Lopez-Blanco R, Velasco M A, Mendez-Guerrero A, et al. Smartwatch for the analysis of rest tremor in patients with Parkinson's disease. J Neurol Sci, 2019, 401: 37-42.
48. 李亮, 俞乾, 徐宝腾, 等. 帕金森病患者多节点运动监测可穿戴设备. 生物医学工程学杂志, 2016, 33(6): 1183-1190.
49. Pardoel S, Kofman J, Nantel J, et al. Wearable-sensor-based detection and prediction of freezing of gait in Parkinson's disease: a review. Sensors (Basel), 2019, 19(23): 5141.
50. Jansen C P, Toosizadeh N, Mohler M J, et al. The association between motor capacity and mobility performance: frailty as a moderator. Eur Rev Aging Phys Act, 2019, 16(1): 1-8.
51. Pahwa R, Bergquist F, Horne M, et al. Objective measurement in Parkinson's disease: a descriptive analysis of Parkinson's symptom scores from a large population of patients across the world using the Personal KinetiGraph®. J Clin Mov Disord, 2020, 7: 1-8.
52. Turakhia M P, Desai M, Hedlin H, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J, 2019, 207: 66-75.
53. Gentili C, Valenza G, Nardelli M, et al. Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: a pilot study. J Affect Disord, 2017, 209: 30-38.
54. Zheng W L, Liu W, Lu Y, et al. EmotionMeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern, 2019, 49(3): 1110-1122.
55. Walch O, Huang Y, Forger D, et al. Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep, 2019, 42(12): zsz180.
56. Danzig R, Wang M, Shah A, et al. The wrist is not the brain: estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient- and device-specific factors. J Sleep Res, 2020, 29(1): e12926.
57. De Zambotti M, Cellini N, Menghini L, et al. Sensors capabilities, performance, and use of consumer sleep technology. Sleep Med Clin, 2020, 15(1): 1-30.
58. Sen-Gupta E, Wright D E, Caccese J W, et al. A pivotal study to validate the performance of a novel wearable sensor and system for biometric monitoring in clinical and remote environments. Digit Biomark, 2019, 3(1): 1-13.
59. Kobsar D, Charlton J M, Tse C T F, et al. Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis. J Neuroeng Rehabil, 2020, 17(1): 1-21.
60. Mc Ardle R, Del Din S, Galna B, et al. Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors?. Gait Posture, 2020, 76: 372-376.
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