Automatic and continuous blood pressure monitoring via an optical-fber-sensor-assisted smartwatch
doi: 10.1186/s43074-023-00099-z
Automatic and continuous blood pressure monitoring via an optical-fber-sensor-assisted smartwatch
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Abstract:
Automatic and continuous blood pressure monitoring is important for preventing cardiovascular diseases such as hypertension. The evaluation of medication effects and the diagnosis of clinical hypertension can both benefit from continuous monitoring. The current generation of wearable blood pressure monitors frequently encounters limitations with inadequate portability, electrical safety, limited accuracy, and precise position alignment. Here, we present an optical fiber sensor-assisted smartwatch for precise continuous blood pressure monitoring. A fiber adapter and a liquid capsule were used in the building of the blood pressure smartwatch based on an optical fiber sensor. The fiber adapter was used to detect the pulse wave signals, and the liquid capsule was used to expand the sensing area as well as the conformability to the body. The sensor holds a sensitivity of -213µw/kPa, a response time of 5 ms, and high reproducibility with 70,000 cycles. With the assistance of pulse wave signal feature extraction and a machine learning algorithm, the smartwatch can continuously and precisely monitor blood pressure. A wearable smartwatch featuring a signal processing chip, a Bluetooth transmission module, and a specially designed cellphone APP was also created for active health management. The performance in comparison with commercial sphygmomanometer reference measurements shows that the systolic pressure and diastolic pressure errors are -0.35 ± 4.68 mmHg and -2.54 ± 4.07 mmHg, respectively. These values are within the acceptable ranges for Grade A according to the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The smartwatch assisted with an optical fiber is expected to offer a practical paradigm in digital health.
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Key words:
- Blood pressure /
- Optical fiber sensor /
- Smartwatch
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[1] Ma L-Y, Chen W-W, Gao R-L, Liu L-S, Zhu M-L, Wang Y-J, Wu Z-S, Li H-J, Gu D-F, Yang Y-J. China cardiovascular diseases report 2018: an updated summary. J Geriatr Cardiology. 2020;17:1. [2] Radovanovic CAT, Santos LAd, Carvalho MDdB, Marcon SS. Arterial Hypertension and other risk factors associated with cardiovascular diseases among adults. Revista latino-americana de enfermagem. 2014;22: 547–553. [3] Liu S, Li Y, Zeng X, Wang H, Yin P, Wang L, Liu Y, Liu J, Qi J, Ran S. Burden of cardiovascular diseases in China, 1990–2016: findings from the 2016 global burden of disease study. JAMA Cardiology. 2019;4:342–52. [4] Allender S, Scarborough P, Peto V, Rayner M, Leal J, Luengo-Fernandez R, Gray A. European Heart. Network. 2008;3:11–35. [5] Zhou B, Carrillo-Larco RM, Danaei G, Riley LM, Paciorek CJ, Stevens GA, Gregg EW, Bennett JE, Solomon B, Singleton RK. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398:957–80. [6] Imai Y, Sasaki S, Minami N, Munakata M, Hashimoto J, Sakuma H, Sakuma M, Watanabe N, Imai K, Sekino H. The accuracy and performance of the A&D TM 2421, a new ambulatory blood pressure monitoring device based on the cuff-oscillometric method and the Korotkoff sound technique. Am J Hypertens. 1992;5:719–26. [7] Geddes L, Voelz M, Combs C, Reiner D, Babbs CF. Characterization of the oscillometric method for measuring indirect blood pressure. Ann Biomed Eng. 1982;10:271–80. [8] Chan G, Cooper R, Hosanee M, Welykholowa K, Kyriacou PA, Zheng D, Allen J, Abbott D, Lovell NH, Fletcher R. Multi-site photoplethysmography technology for blood pressure assessment: challenges and recommendations. J Clin Med. 2019;8:1827. [9] Xing X, Sun M. Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. Biomed Opt Express. 2016;7:3007–20. [10] Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28:R1. [11] Ma Y, Zheng Q, Liu Y, Shi B, Xue X, Ji W, Liu Z, Jin Y, Zou Y, An Z. Self-powered, one-stop, and multifunctional implantable triboelectric active sensor for real-time biomedical monitoring. Nano Lett. 2016;16:6042–51. [12] Fang Y, Zou Y, Xu J, Chen G, Zhou Y, Deng W, Zhao X, Roustaei M, Hsiai TK, Chen J. Ambulatory cardiovascular monitoring via a machine-learning-assisted textile triboelectric sensor. Adv Mater. 2021;33:2104178. [13] Cheng X, Xue X, Ma Y, Han M, Zhang W, Xu Z, Zhang H, Zhang H. Implantable and self-powered blood pressure monitoring based on a piezoelectric thinfilm: simulated, in vitro and in vivo studies. Nano Energy. 2016;22:453–60. [14] Kaniusas E, Pfutzner H, Mehnen L, Kosel J, Tellez-Blanco C, Varoneckas G, Alonderis A, Meydan T, Vázquez M, Rohn M. Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG. IEEE Sens J. 2006;6:819–28. [15] Kim K-H, Hong SK, Jang N-S, Ha S-H, Lee HW, Kim J-M. Wearable resistive pressure sensor based on highly flexible carbon composite conductors with irregular surface morphology. ACS Appl Mater Interfaces. 2017;9:17499–507. [16] Lo LW, Shi H, Wan H, Xu Z, Tan X, Wang C. Inkjet-printed soft resistive pressure sensor patch for wearable electronics applications. Adv Funct Mater. 2020;5:1900717. [17] Rao KS, Samyuktha W, Vardhan DV, Naidu BG, Kumar PA, Sravani KG, Guha K. Design and sensitivity analysis of capacitive MEMS pressure sensor for blood pressure measurement. Microsyst Technol. 2020;26:2371–9. [18] Kim J, Chou EF, Le J, Wong S, Chu M, Khine M. Soft wearable pressure sensors for beat-to-beat blood pressure monitoring. Adv Funct Mater. 2019;8:1900109. [19] Wang C, Li X, Hu H, Zhang L, Huang Z, Lin M, Zhang Z, Yin Z, Huang B, Gong H. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nat Biomed Eng. 2018;2:687–95. [20] Zhang L, Pan J, Zhang Z, Wu H, Yao N, Cai D, Xu Y, Zhang J, Sun G, Wang L. Ultrasensitive skin-like wearable optical sensors based on glass micro/nanofibers. Opto-Electron. Adv. 2020;3:190022–1–190022–7. [21] Guo J, Zhou B, Zong R, Pan L, Li X, Yu X, Yang C, Kong L, Dai Q. Stretchable and highly sensitive optical strain sensors for human-activity monitoring and healthcare. ACS Appl Mater Interfaces. 2019;11:33589–98. [22] Tang Y, Liu H, Pan J, Zhang Z, Xu Y, Yao N, Zhang L, Tong L. Optical Micro/Nanofiber-Enabled Compact Tactile Sensor for Hardness Discrimination. ACS Appl Mater Interfaces. 2021;13:4560–6. [23] Guo J, Liu X, Jiang N, Yetisen AK, Yuk H, Yang C, Khademhosseini A, Zhao X, Yun SH. Highly stretchable, strain sensing hydrogel optical fibers. Adv Mater. 2016;28:10244–9. [24] Park S-J, Lee C-H, Jeong K-T, Park H-J, Ahn J-G, Song K-H. Fiber-to-the-home services based on wavelength-division-multiplexing passive optical network. J Lightwave Technol. 2004;22:2582. [25] Zhu HT, Zhan LW, Dai Q, Xu B, Chen Y, Lu YQ, Xu F. Self-Assembled Wavy Optical Microfiber for Stretchable Wearable Sensor. Adv Opt Mater. 2021;9:2002206. [26] Haseda Y, Bonefacino J, Tam H-Y, Chino S, Koyama S, Ishizawa H. Measurement of pulse wave signals and blood pressure by a plastic optical fiber FBG sensor. Sensors. 2019;19:5088. [27] Pang Y-N, Liu B, Liu J, Wan S, Wu T, Yuan J, Xin X, He XD, Wu Q. Singlemode-multimode-singlemode optical fiber sensor for accurate blood pressure monitoring. J Lightwave Technol. 2022;40:4443–50. [28] Fan X, Huang Y, Ding X, Luo N, Li C, Zhao N, Chen SC. Alignment-free liquid-capsule pressure sensor for cardiovascular monitoring. Adv Funct Mater. 2018;28:1805045. [29] Fuard D, Tzvetkova-Chevolleau T, Decossas S, Tracqui P, Schiavone P. Optimization of poly-di-methyl-siloxane (PDMS) substrates for studying cellular adhesion and motility. Microelectron Eng. 2008;85:1289–93. [30] Mata A, Fleischman AJ, Roy S. Characterization of polydimethylsiloxane (PDMS) properties for biomedical micro/nanosystems. Biomed Microdevices. 2005;7:281–93. [31] Regehr KJ, Domenech M, Koepsel JT, Carver KC, Ellison-Zelski SJ, Murphy WL, Schuler LA, Alarid ET, Beebe DJ. Biological implications of polydimethylsiloxane-based microfluidic cell culture. Lab Chip. 2009;9:2132–9. [32] Yan H, Wang Y, Fufeng L, Gong A, Yun F, Hong Y, Jin X, Cheng Y, Lei H, Zhaoxia X. Relationship of optimal pulse-taking pressure among cun, guan, chi pulse of 264 healthy undergraduates. China Journal of Traditional Chinese Medicine and Pharmacy. 2006. [33] Wang J, Liu K, Sun Q, Ni X, Ai F, Wang S, Yan Z, Liu D. Diaphragm-based optical fiber sensor for pulse wave monitoring and cardiovascular diseases diagnosis. J Biophotonics. 2019;12: e201900084. [34] Song Z, Li W, Bao Y, Wang W, Liu Z, Han F, Han D, Niu L. Bioinspired microstructured pressure sensor based on a janus graphene film for monitoring vital signs and cardiovascular assessment. Adv Electron Mater. 2018;4:1800252. [35] Fu Y, Zhao S, Wang L, Zhu R. A wearable sensor using structured silver-particle reinforced PDMS for radial arterial pulse wave monitoring. Adv Healthc Mater. 2019;8:1900633. [36] Liu Y, Meng F, Zhou Y, Mugo SM, Zhang Q. Graphene oxide films prepared using gelatin nanofibers as wearable sensors for monitoring cardiovascular health. Adv Mater Techol-US. 2019;4:1900540. [37] Sun Y, Dong Y, Gao R, Chu Y, Zhang M, Qian X, Wang X. Wearable pulse wave monitoring system based on MEMS sensors. Micromachines. 2018;9:90. [38] Li Y, Wang Z, Zhang L, Yang X, Song J. Characters available in photoplethysmogram for blood pressure estimation: beyond the pulse transit time. Phys Eng Sci Med. 2014;37:367–76. [39] Li L, Liu Y, Song C, Sheng S, Yang L, Yan Z, Hu DJJ, Sun Q. Wearable alignment-free microfiber-based sensor chip for precise vital signs monitoring and cardiovascular assessment. Adv Fiber Mater. 2022;4:475–86. [40] Zeng Z, Huang Z, Leng K, Han W, Niu H, Yu Y, Ling Q, Liu J, Wu Z, Zang J. Nonintrusive monitoring of mental fatigue status using epidermal electronic systems and machine-learning algorithms. ACS Sens. 2020;5:1305–13. [41] Xu Z, Liu J, Chen X, Wang Y, Zhao Z. Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network. Comput Ind. 2017;89:50–9. [42] Chen C-T, Lin W-L, Kuo T-S, Wang C-Y. Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks. IEEE Trans Biomed Eng. 1997;44:601–9. [43] Wang J-Z, Wang J-J, Zhang Z-G, Guo S-P. Forecasting stock indices with back propagation neural network. Expert Syst Appl. 2011;38:14346–55. [44] Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ, Zheng PP, Fu G, Lee K, Feng Z-P. Smartphone-based blood pressure measurement using transdermal optical imaging technology. Cardiovascular Imaging. 2019;12: e008857. [45] O’Brien E, Petrie J, Littler W, de Swiet M, Padfield PL, Altman D, Bland M, Coats A, Atkins N. The British Hypertension Society protocol for the evaluation of blood pressure measuring devices. J hypertens. 1993;11:S43–62. [46] Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. 1986;327:307–10. [47] Kao Y-H, Tu T-Y, Chao PC-P, Lee Y-P, Wey C-L. Optimizing a new cuffless blood pressure sensor via a solid–fluid-electric finite element model with consideration of varied mis-positionings. Microsystem Technologies. 2016;22:1437–1447. [48] Koyama S, Ishizawa H, Fujimoto K, Chino S, Kobayashi Y. Influence of individual differences on the calculation method for FBG-type blood pressure sensors. Sensors. 2016;17:48. [49] Wang T-W, Lin S-F. Wearable piezoelectric-based system for continuous beat-to-beat blood pressure measurement. Sensors. 2020;20:851. -