Citation: | Tan F Z, Lyu W M, Chen S Y, Liu Z Y, Yu C Y. Contactless vital signs monitoring based on few-mode and multi-core fibers. Opto-Electron Adv 3, 190034 (2020). doi: 10.29026/oea.2020.190034 |
[1] | Khan Y, Ostfeld A E, Lochner C M, Pierre A, Arias A C. Monitoring of vital signs with flexible and wearable medical devices. Adv Mater 28, 4373-4395 (2016). doi: 10.1002/adma.201504366 |
[2] | Tsai S Y, Lee C H, Chen P H, Chung K H, Huang S H et al. Risk factors for early cardiovascular mortality in patients with bipolar disorder. Psychiatry Clin Neurosci 71, 716-724 (2017). doi: 10.1111/pcn.12538 |
[3] | Wise R A, Chapman K R, Scirica B M, Schoenfeld D A, Bhatt D L et al. Long-term evaluation of the effects of aclidinium bromide on major adverse cardiovascular events and COPD exacerbations in patients with moderate to very severe COPD: rationale and design of the ASCENT COPD study. Chronic Obstr Pulm Dis 5, 5-15 (2018). |
[4] | Zhang F X, Yu Y, Zhong J. Research status and development prospects of human vital signs monitoring clothing. IOP Conf Se Earth Environ Sci 233, 042031 (2019). doi: 10.1088/1755-1315/233/4/042031 |
[5] | Wang Z H, Yang Z C, Dong T. A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors (Basel) 17, 341 (2017). doi: 10.3390/s17020341 |
[6] | Guay P, Gorgutsa S, LaRochelle S, Messaddeq Y. Wearable contactless respiration sensor based on multi-material fibers integrated into textile. Sensors (Basel) 17, 1050 (2017). doi: 10.3390/s17051050 |
[7] | Corbishley P, Rodriguez-Villegas E. Breathing detection: towards a miniaturized, wearable, battery-operated monitoring system. IEEE Trans Biomed Eng 55, 196-204 (2008). doi: 10.1109/TBME.2007.910679 |
[8] | Mimoz O, Benard T, Gaucher A, Frasca D, Debaene B. Accuracy of respiratory rate monitoring using a non-invasive acoustic method after general anaesthesia. Br J Anaesth 108, 872-875 (2012). doi: 10.1093/bja/aer510 |
[9] | Pourbabaee B, Roshtkhari M J, Khorasani K. Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans Syst Man Cybern Syst 48, 2095-2104 (2018). doi: 10.1109/TSMC.2017.2705582 |
[10] | Vu E L, Rusin C G, Penny D J, Kibler K K, Easley R B et al. A novel electrocardiogram algorithm utilizing st-segment instability for detection of cardiopulmonary arrest in single ventricle physiology: a retrospective study. Pediatr Crit Care Med 18, 44-53 (2017). doi: 10.1097/PCC.0000000000000980 |
[11] | Haberman Z C, Jahn R T, Bose R, Tun H, Shinbane J S et al. Wireless smartphone ECG enables large-scale screening in diverse populations. J Cardiovasc Electrophysiol 26, 520-526 (2015). doi: 10.1111/jce.12634 |
[12] | Lochner C M, Khan Y, Pierre A, Arias A C. All-organic optoelectronic sensor for pulse oximetry. Nat Commun 5, 5745 (2014). doi: 10.1038/ncomms6745 |
[13] | Harju J, Tarniceriu A, Parak J, Vehkaoja A, Yli-Hankala A et al. Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation. Physiol Meas 39, 065007 (2018). doi: 10.1088/1361-6579/aac9a9 |
[14] | Liu J, Chen Y Y, Wang Y, Chen X, Cheng J et al. Monitoring vital signs and postures during sleep using wifi signals. IEEE Internet Things J 5, 2071-2084 (2018). doi: 10.1109/JIOT.2018.2822818 |
[15] | Nosrati M, Shahsavari S, Lee S, Wang H, Tavassolian N. A concurrent dual-beam phased-array doppler radar using MIMO beamforming techniques for short-range vital-signs monitoring. IEEE Trans Antennas Propag 67, 2390-2404 (2019). doi: 10.1109/TAP.2019.2893337 |
[16] | Hui X N, Kan E C. Monitoring vital signs over multiplexed radio by near-field coherent sensing. Nat Electron 1, 74-78 (2018). doi: 10.1038/s41928-017-0001-0 |
[17] | Wadhwa N, Chen J G, Sellon J B, Wei D L, Rubinstein M et al. Motion microscopy for visualizing and quantifying small motions. Proc Natl Acad Sci USA 114, 11639-11644 (2017). doi: 10.1073/pnas.1703715114 |
[18] | Chen X L, Shao J Y, An N L, Li X M, Tian H M et al. Self-powered flexible pressure sensors with vertically well-aligned piezoelectric nanowire arrays for monitoring vital signs. J Mater Chem C 3, 11806-11814 (2015). doi: 10.1039/C5TC02173A |
[19] | Sadek I, Seet E, Biswas J, Abdulrazak B, Mokhtari M. Nonintrusive vital signs monitoring for sleep apnea patients: a preliminary study. IEEE Access 6, 2506-2514 (2018). doi: 10.1109/ACCESS.2017.2783939 |
[20] | Garcia I, Zubia J, Durana G, Aldabaldetreku G, Illarramendi M A et al. Optical fiber sensors for aircraft structural health monitoring. Sensors (Basel) 15, 15494-15519 (2015). doi: 10.3390/s150715494 |
[21] | Barrias A, Casas J R, Villalba S. A review of distributed optical fiber sensors for civil engineering applications. Sensors 16, 748 (2016). doi: 10.3390/s16050748 |
[22] | Yang X F, Chen Z H, Elvin C S M, Janice L H Y, Ng S H et al. Textile fiber optic microbend sensor used for heartbeat and respiration monitoring. IEEE Sens J 15, 757-761 (2015). doi: 10.1109/JSEN.2014.2353640 |
[23] | Yu C Y, Xu W, Zhang N, Yu C C. Non-invasive smart health monitoring system based on optical fiber interferometers. In Proceedings of the 2017 16th International Conference on Optical Communications and Networks 1-3 (IEEE, 2017); http://doi.org/10.1109/ICOCN.2017.8121526. |
[24] | Inan O T, Migeotte P F, Park K S, Etemadi M, Tavakolian K et al. Ballistocardiography and seismocardiography: a review of recent advances. IEEE J Biomed Health Inform 19, 1414-1427 (2015). doi: 10.1109/JBHI.2014.2361732 |
[25] | Li G F, Bai N, Zhao N B, Xia C. Space-division multiplexing: the next frontier in optical communication. Adv Opt Photonics 6, 413-487 (2014). doi: 10.1364/AOP.6.000413 |
[26] | Rademacher G, Ryf R, Fontaine N K, Chen H S, Essiambre R J et al. Long-haul transmission over few-mode fibers with space-division multiplexing. J Lightw Technol 36, 1382-1388 (2018). doi: 10.1109/JLT.2017.2786671 |
[27] | Van Newkirk A, Antonio-Lopez E, Salceda-Delgado G, Amezcua-Correa R, Schülzgen A. Optimization of multicore fiber for high-temperature sensing. Opt Lett 39, 4812-4815 (2014). doi: 10.1364/OL.39.004812 |
[28] | Villatoro J, Arrizabalaga O, Durana G, Saez de Ocariz I, Antonio-Lopez E et al. Accurate strain sensing based on super-mode interference in strongly coupled multi-core optical fibres. Sci Rep 7, 4451 (2017). doi: 10.1038/s41598-017-04902-3 |
[29] | Zhao Z Y, Dang Y L, Tang M, Li B R, Gan L et al. Spatial-division multiplexed Brillouin distributed sensing based on a heterogeneous multicore fiber. Opt Lett 42, 171-174 (2017). doi: 10.1364/OL.42.000171 |
[30] | Li A, Wang Y F, Fang J, Li M J, Kim B Y et al. Few-mode fiber multi-parameter sensor with distributed temperature and strain discrimination. Opt Lett 40, 1488-1491 (2015). doi: 10.1364/OL.40.001488 |
[31] | Zhao Z Y, Tang M, Fu S N, Liu S, Wei H F et al. All-solid multi-core fiber-based multipath Mach-Zehnder interferometer for temperature sensing. Appl Phys B 112, 491-497 (2013). doi: 10.1007/s00340-013-5634-8 |
[32] | Chen S Y, Huang Z Y, Tan F Z, Yang T Y, Tu J J et al. Vital signs monitoring using few-mode fiber-based sensors. Proc SPIE 10814, 108140P (2018). |
[33] | Chen S Y, Tan F Z, Huang Z Y, Yang T Y, Tu J J et al. Non-invasive smart monitoring system based on multi-core fiber optic interferometers. In Proceedings of 2018 Asia Communications and Photonics Conference 1-3 (IEEE, 2018); http://doi.org/10.1109/ACP.2018.8595907. |
[34] | Tan F Z, Liu Z Y, Chen S Y, Yu C Y. Vital signs monitoring using twin core fiber-based sensor. In Proceedings of the 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) (IEEE, 2019). |
[35] | Weng Y, Ip E, Pan Z Q, Wang T. Single-end simultaneous temperature and strain sensing techniques based on Brillouin optical time domain reflectometry in few-mode fibers. Opt Express 23, 9024-9039 (2015). doi: 10.1364/OE.23.009024 |
[36] | Yin G L, Lou S Q, Lu W L, Wang X. A high-sensitive fiber curvature sensor using twin core fiber-based filter. Appl Phys B 115, 99-104 (2014). |
The simulated intensity of excited LP01 mode and LP11 mode with offset distance between SMF and TMF from 0 μm to 10 μm (a) and the results of LP01, LP11, LP02 and LP21 modes regarding four-mode fiber (b).
The schematic diagram of FMF in-line interferometers based on the structure of SMF-FMF-SMF, and the intensity distribution of modes supported in FMF, namely LP01, LP11, LP02 and LP21 modes.
The experiment on ER versus offset distance between SMF and FMF to obtain a desired spectrum for sensing (a) and the results regarding TMF: collected spectra with the SMF shifts away from the TMF in transverse direction (b).
Summarized extinction ratio as well as insertion loss variation when the SMF shifts away from the TMF in transverse direction (a) and the spectra under different lengths of TMF from 0.8 m to 0.4 m (b).
Collected optical spectra under different offset distances between SMF and four-mode fiber (a) and the summarized extinction ratio as well as insertion loss variation with offset distance (b).
Schematic diagrams of two MCF in-line interferometers: the TCF interferometer in the structure of SMF-TCF-SMF and the corresponding output optical spectrum (a), the SCF in-line interferometer based on the structure of SMF-MMF-SCF-MMF-SMF (b).
Optical spectra under different offset distances between SMF and TCF (a) and the desired spectrum obtained in the SCF in-line interferometer (b).
Summarized extinction ratio as well as insertion loss variation when the SMF shifts away from the TCF in transverse direction (a) and the extinction ratio and insertion loss variation with different MMF lengths in SCF interferometer (b).
Vital signs monitoring experimental setup, including the in-line optical fiber interferometers based on FMF and MCF, which are placed under the mattress, a TLS for wavelength tuning and a photodetector as well as a DAQ card for data collection.
Vital signs monitoring results using the TMF in-line interferometer, including the recovered respiration and heartbeat waveform, obtained RR (9 bpm) and HR (66 bpm) and interval errors (a), and the same results regarding the four-mode fiber interferometer (b).
Vital signs monitoring results using the TCF interferometer, including the raw data, recovered respiration and heartbeat waveform for RR and HR calculation (a) and the respiration monitoring results using the SCF interferometer (b).
Post-exercise physiological activities characterization results using the TCF in-line interferometer, including the recovered heartbeat waveform (a) and respiration waveform (b) and calculated HR and RR.
Curvature sensing results based on the TCF in-line interferometer: different sensitivities are observed under different bending direction from 0° to 360°, for example the wavelength shift results under the curvature from 0 m-1 to 1 m-1 indicates the sensitivity of 18 nm/m-1 under the orientation angle of 0°.